Nasal microbiota homeostasis regulates host anti-influenza immunity via the IFN and autophagy pathways in beagles

Nasal microbiota homeostasis regulates host anti-influenza immunity via the IFN and autophagy pathways in beagles | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Nasal microbiota homeostasis regulates host anti-influenza immunity via the IFN and autophagy pathways in beagles Jinzhu Geng, Yuhao Dong, Hao Huang, Xia Wen, Ting Xu, Yanbing Zhao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4612057/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jan, 2025 Read the published version in Microbiome → Version 1 posted 4 You are reading this latest preprint version Abstract Background The respiratory tract houses a specialized microbial ecosystem, and despite the close anatomical and physiological ties between the oral, upper respiratory, and lower respiratory tracts, there is a substantial discrepancy in microbial quantity, spanning multiple orders of magnitude. The potential for commensal bacteria to prevent infection lies in their ability to regulate innate and adaptive host immune responses. Influenza virus predominantly targets and replicates within the epithelial cells of both upper and lower respiratory tracts. Given this, we hypothesize that the nasal-lung-microbe cross-talk plays a crucial role in influencing influenza susceptibility. In this study, we investigated viral presence, gene expression profiles of host, and the nasal and lung microbiota in a beagle dog model with antibiotic-induced nasal dysbiosis during influenza virus infection. Results In this study, using 16S rRNA sequencing, combined with comparative anatomy, transcriptomics and histological examination, we investigated viral presence, gene expression profiles of host, and the nasal and lung microbiota in influenza-infected 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 worsens the influenza-induced disturbance in lung microbiota. Further, we identified one strain of Lactobacillus plantarum with a significant antiviral effect, which is exerted by activating the IFN pathway and modulating the impaired autophagy flux induced by influenza virus. Our data collectively indicate a close connection between the microbiomes of different ecological niches in the nasal and lung regions. This connection significantly influences subsequent host-microbe cross-talk, which was associated with an increased susceptibility to influenza. Conclusions Our investigation reveals that nasal microbiota dysbiosis not only increases host susceptibility to influenza virus infection but also contributes to the exacerbation of influenza-induced lung microbiota dysregulation. This intricate relationship extends to the microbiome composition, demonstrating correlations with critical factors such as host antiviral responses, inflammation thresholds, and mucosal barrier integrity. Together, these findings underscore the substantial impact of nasal microbiota dysbiosis on the overall outcome during influenza infections. Influenza Respiratory microbiome IFN Autophagy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 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, referred to as 'colonization resistance,' could play a crucial role in maintaining respiratory health[ 6 ]. Furthermore, numerous studies have demonstrated the intricate and complex interactions between microorganisms and hosts in different ecological niches, such as the brain-gut axis, lung-gut axis, and liver-gut axis. This further enhances our understanding of symbiotic microbes within the human body[ 7 – 9 ]. The oral and upper respiratory tracts are closely linked anatomically and physiologically with the lower respiratory tract including the lung, and the influence of oral and upper respiratory microbes on the lung microbiota is increasingly being recognized[ 10 ]. Previous studies have reported correlations between microbiome composition and various physiological characteristics and disease manifestations in the nasal cavity and lung[ 4 , 10 ]. Notably, unlike the oral cavity and gut, which harbor a substantial bacterial load teeming with millions of viable and replicating microbes, the lower airways have been considered a sterile environment because of the necessity that a thin bronchial epithelial fluid lining is maintained to enable efficient gas exchange[ 11 ]. However, with the advancement in microbiome research, it is increasingly acknowledged that the pulmonary environment hosts a diverse microbial community[ 12 ]. 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[ 13 ]. 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 , 14 ]. The dysbiosis of the airway microbiota has been linked to the acceleration of lung function decline in chronic obstructive pulmonary disease[ 15 ]. 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[ 16 , 17 ]. Several studies have indicated an association between coronavirus disease (COVID-19)[ 18 – 20 ], respiratory syncytial virus (RSV)[ 18 – 20 ] or influenza A virus (IAV)[ 18 – 20 ] infection and the upper respiratory tract 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[ 21 ]. 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 three-month-old beagles by locally applying a combination of mupirocin and neomycin ointment to the nasal cavity, and revealed that nasal microbiota dysbiosis diminishes the interferon (IFN)-mediated antiviral response, exacerbates influenza-induced mucosal barrier disruption, and heightens host susceptibility to influenza virus. Interestingly, we observed that nasal microbiota dysbiosis exacerbates lung microbiota dysbiosis after influenza virus infection, lowers the inflammation threshold in the lung, and worsens clinical manifestations and pulmonary pathology. Furthermore, we have identified two commensal bacterial genus, 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 virus infection. Further study reveals a key role of Lactobacillus plantarum C123( L.p ) in modulating the antiviral response. As far as we know, such correlation has not been reported to date. Methods Virus, bacteria and cell strains The viral strain of the H3N2 subtype, A/Canine/Jiangsu/06/2010 (JS/10), was used for 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) cells and titered by plaque assay as previously reported[ 22 ]. The detailed information on lactic acid bacteria in this study is shown in Supplementary Table 1. A549 cells (ATCC CCL-185) were cultured in F-12K complete medium (90% F-12K + 10% FBS), while canine kidney cells (MDCK, ATCC CCL-34) and 293T cells (ATCC CRL-3216) were cultured in DMEM complete medium (90% DMEM + 10% FBS) Animals and grouping Three-month-old conventional beagle puppies ( Anlimao Biotechnology, Yizheng, China) in this study 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[ 23 ]. With a subsequent routine examination at the veterinary teaching hospital of Nanjing Agricultural University, these animals were demonstrated to be negative for canine coronavirus, canine parvovirus, canine distemper virus, and canine parainfluenza virus. Experimental dogs were collectively housed at the experimental animal center of Nanjing Agricultural University for one week. Subsequently, they were randomly divided into three groups (Nor, WT and Abx) and placed in separate rooms. Each group comprises 9 dogs, consisting of 4 females and 5 males. The Nor group functions as the experimental negative control, receiving no interventions (neither antibiotic treatment nor virus inoculation). Furthermore, during the respective stages (antibiotic treatment and virus inoculation phases), PBS is employed as a substitute for antibiotics and virus treatment. The WT group, during the antibiotic treatment phase, substitutes PBS for antibiotics. In the virus infection stage, intranasal inoculation was performed with 1 mL of JS/10 at a concentration of 10 7 PFU/ mL. The Abx group received continuous nasal application of mupirocin and neomycin ointment (10 mg per nostril, administered twice a day) for three days. On the challenge day (day 0), nasal samples were collected from the dogs, followed by intranasal inoculation with 1 mL of JS/10 at a concentration of 10 7 PFU/mL for the Abx and WT groups. 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). 16S rRNA gene sequencing For the nasal microbiota collection at three stages (pre-antibiotic treatment, post-antibiotic treatment, and post-virus infection), samples were acquired from three-month-old Beagles using sterile gloves (Qiagen). Following sampling, swabs were promptly returned to the collection tube and stored at − 80°C until retrieval for subsequent analysis. Concerning the extraction of genomic DNA from the nasal microbiota, nasal swabs were vortexed for 2 minutes in 1 mL of modified liquid Amies transport medium. Subsequently, 500 µL samples from each swab were centrifuged at 13,000 × g at 4°C for 10 minutes. The resulting pellets were then dissolved in Buffer ALT with lysozyme (Sigma) and lysostaphin (Sigma) and incubated for 30 minutes at 37°C, followed by DNA extraction in accordance with 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) sequencing platform. For the lung microbiota collection, lung tissue sampling was performed using the biopsy punch plunger (Φ 4.mm, Miltex) according to a pre-designated experimental protocol (Fig. 4 A). The sampled tissues were promptly placed into sterile 1.5 mL EP tubes and stored at -80°C until retrieval for 16S rRNA gene sequencing. For the extraction of genomic DNA from the lung microbiota, lung tissues were homogenized for 10 minutes in 1 mL of modified liquid Amies transport medium. Subsequently, 500 µL aliquots from each homogenate were centrifuged at 1,000 × g at 4°C for 10 minutes to remove large tissue fragments. Following this, another set of 500 µL aliquots from each homogenate was centrifuged at 13,000 × g at 4°C for 10 minutes. The DNA extraction process and 16S rRNA gene sequencing followed the same steps as in nasal microbiota extraction. Nasal bacterial load analysis Bacterial loads were quantified in triplicate by SYBR green qPCR as previously described[ 24 , 25 ]. Briefly, Quantitative PCR (qPCR) was performed using the StepOnePlus (Applied Biosystems) and the AceQ qPCR SYBR Green Master Mix (High ROX Premixed, Vazyme). A 253 bp product was generated using the primers: 520F, 5’- AYTGGGYDTAAAGNG and 820R, 5’-TACNVGGGTATCTAATCC. All reactions were performed in triplicate and included standards and non-template controls. Standards were generated from near full length cloned 16S rRNA gene of Escherichia coli DH5α. Plasmids quantified using Quantit picogreen dsDNA Assay kit (Promega, Madison, USA), samples were then serially diluted 10-fold to form standards ranging from 1×10 8 -1×10 4 . Reactions consisted of 10 µL of SYBR Fast qPCR Master mix, 0.4 µL of 10µM dilutions primers and 7.2 µL of nuclease free PCR water (Biosharp). 2 µL of template DNA was added to each reaction. Cycling conditions were: 90°C for 5 minutes followed by 40 cycles of: 95°C for 30 seconds, 50°C for 15 seconds, and 72°C for 15 seconds. Melt curves were run as default from 60°C to 95°C, over 15 minutes. Microbiota sequence data analysis The downstream amplicon bioinformatic analyses were performed with EasyAmplicon pipeline (v1.15)[ 26 ]. The nonredundant sequences were denoised into ASVs via the -unoise3 command of USEARCH (v10.0)[ 27 ]. The feature table was created with VSEARCH (v2.15)[ 28 ]. 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) [ 29 ]. 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) was utilized to identify significantly differential features between groups, and the Benjamini-Hochberg or Tukey-Kramer method was used to control the FDR [ 30 ]. The R package igraph (v1.5.1, https://github.com/igraph ) was utilized for differential ASVs 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[ 31 ]. 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. R package ggplot2(v3.4.3) was utilized for visualization of the identified pathways. The Pearson correlation between statin-associated pathways and important statin-associated 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 haematoxylin 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), 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) for 20 min. Then samples were blocked with the blocking buffer (Beyotime) 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 (Ptoteintech) or anti-mouse secondary antibody conjugated with Alexa Fluor 555 (Ptoteintech) at room temperature for 1 h. In this study, the following primary antibodies were used at 1:100 dilution: influenza virus NP mouse polyclonal antibody (laboratory preparation), TJP1/ZO-1 rabbit polyclonal antibody (Bioss), MAP1LC3B/LC3B rabbit monoclonal antibody (Abmart) or SQSTM1/p62 mouse monoclonal antibody (Abmart). After washing in PBS, nuclei were counterstained with DAPI (4',6-diamidino-2-phenylindole) and coverslips were mounted with Antifade Mounting Medium (Beyotime). Images were acquired using a Zeiss LSM 970 microscope, following morphometric analysis employing ZEISS ZEN (v3.9) and ImageJ[ 32 ]. Western blot analysis The procedure of Western blot was performed as described earlier. Briefly, after protein samples were extracted from tissues, the concentration of protein was determined spectrophotometrically at 562 nm using the Protein Quantitative Reagent Kit-BCA Method (Epizyme Biomedical Technology). All samples were diluted to the same concentration and added to 5× SDS-PAGE sample loading buffer (Beyotime). 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). The membranes were blocked in 5% (w/v) non-fat milk in TBST (TBS + 0.1% (v/v) Tween-20) and incubated overnight with the primary antibodies at 4°C. The following primary antibodies were used at 1:1000 dilution: anti-M1, anti-NP (laboratory preparation), phospho-TBK1 (Ser172) (Abmart), TBK1 (Abmart), phospho-IRF3 (S386) (Abmart), IRF3 (Abmart), phospho-NF-κB/p65 (Ser536) (Abmart) and NF-κB/p65 (Ser536) (Abmart) antibodies, MAP1LC3B/LC3B (Abmart), SQSTM1/p62 (Abmart), GAPDH antibody (Abmart) was used as loading controls. After washing in TBST, the horseradish peroxidase (HRP)-conjugated anti-rabbit or anti-mouse secondary antibody (Ptoteintech) were used at 1:5000 dilution for 90 min at room temperature. Relative protein expression was quantified by ECL (Epizyme Biomedical Technology) and visualized on the ChemiDoc Imaging system (Bio-Rad). Band densitometry was performed on the ImageJ software and normalized for the control group. Quantification of mRNA expression Total RNA was isolated from the tissue, whole blood with Total RNA Kit (Omega Bio-tek), 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). The cDNA was diluted 1:10 (v/v) by RNase-free water (Biosharp). Relative mRNA expression was quantified by qPCR analyses on a StepOnePlus (Applied Biosystems), using AceQ qPCR SYBR Green Master Mix (High ROX Premixed, Vazyme). 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 2. RNA sequencing (RNA-seq) analysis Utilizing the Trizol method, total RNA from eukaryotic organisms was extracted. After purity assessment via NanoDrop One, the integrity of the extracted RNA was evaluated employing the Agilent 2100 systems. The mRNA from the total RNA pool was enriched and purified with the VAHTS mRNA Capture Beads reagent kit (Vazyme), and then was fragmented via ion shearing to achieve fragments within the range of 250–450 base pairs, 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 12K (Applied Biosystems) fluorescence quantification assays. Ultimately, second-generation sequencing was conducted on the Illumina NovaSeq 6000 platform (Illumina), 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). Alignment to the reference genome (Dog10K_Boxer_Tasha, GCF_000002285.5) was executed through STAR (v2.7.10). 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). Enrichment analyses for KEGG and GSEA pathways were conducted via clusterProfiler (v4.8.2)[ 33 ]. Differential gene clustering based on k-means clustering occurred through the STRING online platform ( https://cn.string-db.org/ ) and ClusterGVis(v0.0.2)[ 34 ]. All plots were generated using ggplot2 (v3.4.3) and pheatmap (v1.0.12). Co-infection assay in vitro A549 cells were cultured in F12K medium (Gibco) supplemented with 10% fetal bovine serum (Gibco). Seeded at a density of 1.0×10 6 cells/ml in 2 mL in 6-well plates, cells were incubated at 37°C with 5% CO 2 until attaining 80% confluency. Following PBS washes, cells were infected with JS/10 at an MOI = 1 and incubated for 1 hour at 37°C with 5% CO 2 , then washed thrice with PBS and replenished with 2 mL of virus infection maintenance medium (0.125 µg/mL TPCK-trypsin in Opti-MEM). Lactic acid bacteria were statically incubated at 37°C in MRS broth (Sigma-Aldrich) until reaching an OD 600 of 0.5. Subsequent PBS washes were followed by resuspension in virus infection maintenance medium, adjusting the OD 600 to 1. The bacterial suspension, at an MOI of 10, was subsequently added to A549 cells previously infected with JS/10. At specific time points, cell lysates were collected using RIPA buffer (containing 1% protease inhibitor and phosphatase inhibitor), and the total protein concentration was determined using the BCA assay. 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 hours post-transfection, the cells were washed with PBS and then infected with JS/10 at an MOI of 1. After a 1-hour incubation at 37°C with 5% CO 2 , the cells were washed three times with PBS and replenished with 0.5 mL of virus infection maintenance medium (0.125 µg/mL TPCK-trypsin in Opti-MEM). Lactic acid bacteria were statically incubated at 37°C in MRS broth (Sigma-Aldrich) until reaching an OD 600 of 0.5. The cultures were then washed with PBS and resuspended in virus infection maintenance medium, adjusting the OD 600 to 1. This bacterial suspension was added to A549 cells that had been previously infected with JS/10. At 24 hours post-infection, the cells were lysed with lysis buffer, and luciferase activities were measured using a dual luciferase assay kit (Promega, Madison, WI, USA) according to the manufacturer's instructions. Cytotoxicity assay Cell viability was detected using CCK-8 assay (Beyotime). In brief, A549 cells were cultured in F12K medium (Gibco) supplemented with 10% fetal bovine serum (Gibco). Seeded at a density of 0.5 × 10 5 cells/mL in 100 µL in 96-well plates, cells were incubated at 37°C with 5% CO 2 until attaining 80% confluency. Then it was infected with 100 µL of bacterial suspension at an MOI of 1:10 and virus suspension at an MOI of 1:1 and incubated for 12 hours or 24 hours at 37°C with 5% CO 2 . 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 or cells infected with influenza virus alone. Lentiviral transduction 293T cells were cultured in DMEM medium (Gibco) supplemented with 10% fetal bovine serum (Gibco) and seeded at a density of 1.0×10 6 cells/mL in 2 mL in 6-well plates. Subsequently, the plasmids plvx-GFP-RFP-hLC3B, pMD2.G, and pSPAX2 were co-transfected into the 293T cells using Lipofectamine 2000 (Thermo Fisher Scientific). Cell culture supernatants were collected at 48 hours and 72 hours post-transfection, filtered through a 0.45 µm filter, and used to infect A549 cells for 24 hours. Following infection, selection pressure was applied using 2 µg/mL puromycin (MCE), and single-cell clones were obtained using limiting dilution method. The cells were cultured in complete medium (F12K, 10% fetal bovine serum) supplemented with 5 µM/mL rapamycin (MCE) for 12 hours. Autophagosome formation was then observed under a fluorescence microscope. Results Dysbiosis of the nasal microbiome exacerbates influenza-induced respiratory disease To elucidate the potential connection of nasal microbiota homeostasis to influenza susceptibility, we created a model of nasal microbiota dysbiosis in three-month-old beagles by locally applying a combination of mupirocin and neomycin ointment to the nasal cavity. Nasal swabs were collected before and after the antibiotic treatment, and 16S rRNA sequencing was employed to assess the changes in the nasal microbiome. The absolute abundance of nasal microbiota was evaluated via quantitative real-time PCR (RT-qPCR). As anticipated, short-term administration of combination antibiotics in the nasal cavity significantly reduced the microbial absolute abundance (Fig. S1 A). Considering the significant decrease in bacterial abundance following treatment with compounded antibiotics, subsequent analyses were conducted based on the relative abundance obtained through equal-weight resampling. We observed a relatively decreased α-diversity after antibiotic treatment in both Chao1 and Richness indices (Fig. 1 A). Furthermore, principal coordinate analysis (PCoA) based on Bray-Curtis distances indicated significantly different nasal microbial structures compared with those before antibiotic treatment (Fig. 1 B). Then we conducted differential analyses at the phylum and genus levels. We noticed that combination antibiotic treatment caused expansion of the phylum Proteobacteria (Fig. S1 B), which is considered as a signature of gut dysbiosis[ 35 ]. 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. S1 C, p < 0.05, Benjamini-Hochberg). To gain further insights into the potential impact of antibiotic-mediated nasal microbiome dysbiosis on host functionality, the PICRUSt2, a robust tool for predicting functional pathways base on microbial community composition[ 36 ], was utilized to assess the influences of antibiotic treatment on the contributions of microbiomes to host-associated pathways. Subsequently, the EasyAmplicon package was used for KEGG (Kyoto Encyclopedia of Genes and Genomes, https://www.kegg.jp/ ) three-level classification and the DESeq2 package was used for differential analysis at the KEGG third level[ 30 ]. We observed significant differences in the viral infection and apoptosis pathways affected by the microbiomes between before and after antibiotic treatment (Fig. 1 C). Given this, we hypothesize that the nasal microbiome disturbance caused by short-term antibiotic treatment might affect the host's ability to resist influenza infection. To explore this, we administered a combination of antibiotics to beagles and then inoculated in nasal with 10 7 PFU (plaque forming unit) of H3N2 virus (A/canine/Jiangsu/06/2011, JS/10) (Abx group). The dogs without antibiotic treatment but with virus infection were categorized as the infection control group (WT group), and the untreated and uninfected dogs served as the normal control group (Nor group). The experiment was divided into three stages: the Before stage, which indicates the period prior to antibiotic treatment; the Clean stage, representing the period following antibiotic treatment but before viral infection; and the Infected stage, denoting the period after viral infection. A detailed overview of the experimental workflow was provided in Fig. 1 d. 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. 1 E) and weight loss (Fig. 1 F). 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 [ 37 ]. The data indicated significant differences in clinical score among the three groups, with the Abx group presenting the highest clinical score (Fig. 1 G, 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. 1 H, p < 0.001, Tukey's HSD). On the eighth day of infection, extensive hemorrhagic spots were observed 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. 1 I). 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. 1 I, Fig. S2B). Then we scored the histopathological changes in the nasal, tracheal, and lung tissues based on the evaluation criteria outlined and described elsewhere[ 38 ].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. 1 J, 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. 1 K, 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 respiratory tract infection Microbiota residing in the nasal cavity have been reported to be associated with susceptibility to and severity of RTIs [ 1 , 4 , 39 ]. To explore which microbes play a pivotal role in the host's resistance to influenza infection, we characterized the bacterial compositions to reveal differences in the microbial communities among the Nor, WT and Abx groups. The ternary plot indicates that regardless of influenza virus infection, the high-abundance microbial communities (genus level: relative abundance > 0.5%) in the Abx group showed a significant loss after antibiotic treatment (Fig. 2 A, Fig. S5A). Meanwhile, during the entire experimental period, the Chao1 and Richness diversity indices in the Abx group showed a sustained decline, a trend that continued even post-viral infection. (Fig. S4A, B; p < 0.001). To evaluate the similarity of the bacterial communities among the above three groups, the PCoA was performed using the Bray-Curtis distance matrix. 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 0.05, Wilcoxon rank-sum test). To further explore the bacterial genus enriched in the Nor, WT and Abx groups at different stages (Before, Clean, Infected), a differential enrichment analysis was conducted using DESeq2, combined with one-way ANOVA. 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. 2 B, Fig. S5B, p < 0.05, Tukey's HSD). To further ascertain whether this change was attributable to antibiotic treatment or influenza infection, we conducted intra-group comparisons for Abx and WT groups before and after virus infection. Interestingly, we found that, irrespective of the Abx or WT group, the relative abundance of Lactobacillus showed no significant difference before and after virus infection. 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 (Fig. S6, p < 0.05, Tukey's HSD). Then we conducted a correlation analysis on the microbial communities before and after infection in both the Abx and WT groups. After antibiotic treatment, the proportions of Lactobacillus were observed to be negatively associated with Moraxella . Additionally, after virus infection, the proportions of Lactobacillus , Megamonas , Prevotella _9 and Lachnoclostridium were observed to have a negative association with Moraxella (Fig. 2 C, 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. 2 D). 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 after antibiotic treatment. Notably, these differences encompass pathways associated with the infection of pathogenic microorganisms, including Kaposi sarcoma-associated herpesvirus (KSHV) infection, Herpes simplex virus 1 (HSV-1) infection, Hepatitis C virus (Hepacivirus C), Human cytomegalovirus (HCMV) infection, Human immunodeficiency virus 1 (HIV-1) infection, Epstein-Barr virus (EBV) infection, Hepatitis B and Influenza A, cell junctions (tight, adherens, and gap junctions), as well as autophagy and 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. 2 E,). 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 genus 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 deeper 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. 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 up-regulated and 308 genes down-regulated (Fig. 3 A). A total of 2784 DEGs yielded by inter-group comparisons (Fig. 3 B) were subjected to clustering analysis based on the Fuzzy C-means (FCM) algorithm, resulting in the identification of 5 distinct clusters (Fig. 3 C). Notably, Cluster3 and Cluster5 displayed contrasting trends. Specifically, Cluster3 predominantly comprised inflammation-related genes (e.g., NLRP3, IL1β), while Cluster5 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, the gene set enrichment analysis (GSEA) for the Abx group identified a significant enrichment of the genes associated with viral disease, including influenza A, coronavirus disease (COVID-19), Epstein-Barr virus (EBV) infection, Hepatitis C, herpes simplex virus 1 (HSV-1) infection, human immunodeficiency virus 1 (HIV-1), Hepatitis B, and human T-cell leukemia virus 1 (HTLV-1) infection. Additionally, some inflammation-related pathways were enriched, including IL-17, tumor necrosis factor (TNF), RIG-I-like receptor, Toll-like receptor, 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. 3 d). 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. 3 e). In Cluster 1, notably, genes involved in regulating interferon production (IFIH1, IRF1, IRF9, STAT1 and STAT2) and interferon-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. 3 E, p < 0.05, Benjamini-Hochberg). Therefore, we speculate that the disruption of nasal microbiota may, to some extent, attenuate the host's antiviral immune response. Additionally, the transcript level of cytokines and pertinent signaling pathways in the bloodstream following viral infection (Fig. 3 F) showed a consistent trend with that observed in the nasal transcriptome analysis (Fig. 3 E). 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. 3 E, 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[ 40 , 41 ]. 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 virus 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. 3 c, Cluster 3,). Therefore, we further conducted an immunofluorescence analysis of the tight junction protein ZO-1 (TJP1), and demonstrated that ZO-1 expression at the infection site was lower in the Abx group than in the WT group (Fig. 3 G). Disruption of nasal microbiota exacerbates the dysbiosis of lung microbiota following influenza infection. While the upper airway accommodates the most substantial biomass and stable microbial communities, the lungs are continually exposed to these bacteria through micro-aspiration. Given this, we pose a question: Can nasal microbiota disruption lead to changes in lung microbiota, and thus exacerbate 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. 4 A. To investigate the composition and distribution characteristics of lung microbiota among different 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. 4 B). At the genus level, Bifidobacterium , Lactobacillus , Bacillus , Moraxella , Streptococcus , Bacteroides and Nitratireductor constitute the predominant microbial communities in the canine lung (Fig. 4 C). This composition bears resemblance to the microbiota found in the human lung[ 11 , 12 ]. 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). 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. 4 D, Fig. S12B, C, p < 0.05, Tukey-Kramer). 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. 4 E, F). Further, we determine the contribution of pulmonary microbiota to host pathways. The functional analysis of pulmonary microbiota by PICRUSt2 indicates significant differences in several pathways between the Abx group and the WT group. These include the viral infection-related pathway (influenza A, Hepatitis B, Hepatitis C, HCMV infection, EBV infection, KSHV infection, HSV1 infection and HIV infection), autophagy-related pathway (mTOR signaling pathway), apoptosis pathway, cell junctions (tight junction, adherens junction and gap junction), and Toll and Imd signaling pathways (Fig. 4 G, 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. 4 H, 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 in influenza infection To better comprehend the potential impact of lung microbiota dysbiosis following influenza infection, we conducted transcriptome sequencing on three groups of lung tissues and performed differential analysis on mRNA expression matrices using the DEseq2 package. 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 up-regulation while 305 genes exhibited down-regulation in the Abx group (Fig.S14A). The union of DEGs from inter-group comparisons yielded a total of 2,225 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 differentially expressed genes in the lung transcriptome. Cluster 2 includes several canonical interferon-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 differentially expressed genes 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. 3 C). 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 Cluster 1, 2 and 3 (Fig. 5 A). Normalization of the expression levels of these genes based on the Nor group showed that interferon-stimulated genes (ISGs), including OAS1, OAS2, Mx1, Mx2, ISG15, IFIT2, IFIT3 and TRIM25, were significantly downregulated in the Abx group (Fig. 5 B, ISGs, 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. 5 B, p < 0.05, Benjamini-Hochberg). 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[ 42 ], were consistently upregulated in the Abx group (Fig. 5 B, 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. 5 D, 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. 5 C). 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 indicates a significant positive correlation between antivirus-related (IFNβ1, IFNα, OAS1, Mx1, PKR, ISG15, Myd88, Mx2) and inflammation-related (IL6, TNFα, Bax, Caspase3) genes and Lactobacillus (Fig. 5 D, p = 0.4), but a notable negative correlation between the above genes and Moraxella (Fig. 5 D, p = 0.4). Based on Pearson correlation coefficient analysis, Lactobacillus exhibited a significant positive correlation with the transcription of IFNβ1 and Myd88 (Fig. 5 E, Pearson's R > 0.7, p < 0.001, Benjamini-Hochberg), but a significant negative correlation with TNFα and caspase3 transcription (Fig. 5 E, Pearson's R < − 0.7, p < 0.001, Benjamini-Hochberg). In contrast, Moraxella demonstrated a completely 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 (TANK-binding kinase 1) were higher, while the phosphorylation levels of inflammation-related proteins, for example, NF-κB/p65, were lower in the Abx group (Fig. 5 F). This finding further confirms that microbial dysbiosis in the lung diminishes the host's antiviral response. Lactobacillus exerts antiviral effects in vitro by activating IFN-mediated pathways. Lactic acid bacteria (LAB) are known as probiotic organisms and have been increasingly reported to exert powerful biological actions. In this study, we isolated ten strains of LAB from the nasal cavity, oral cavity and rectum of experimental beagles and conducted in vitro antiviral assays in two different setups (Fig. 7 A). Our data showed that Lactobacillus plantarum C123 ( L.p ) exhibited significant in vitro antiviral activity, whether by pre-treating A549 cells before influenza infection or co-infection with influenza virus (Fig. 7 B). Cytotoxicity evaluation using CCK-8 assay showed that Lactobacillus plantarum C123 did not exhibit cytotoxicity against A549 cells. Further, we used the dual-luciferase reporter assay to evaluate IFN-β and NF-κB/p65 promoter activities, and found that Lactobacillus plantarum C123 could significantly enhance the activation of the IFN-β promoter, but not affect the NF-κB/p65 promoter activity (Fig. 7 C). 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 interferons and is subject to tight regulation[ 43 ].To investigate whether the antiviral effects of Lactobacillus plantarum C123 rely on upstream regulation of the IFN pathway, we assessed TBK1 phosphorylation levels in A549 cells following exposure to Lactobacillus plantarum C123 and influenza infection. Interestingly, both total and phosphorylated TBK1 levels increased in the early stages of infection (1h, 4h, 8h) in both the pre-treatment and co-infection groups. However, at later stages (12h, 24h), both total and phosphorylated TBK1 levels significantly decreased in the co-infection group. Additionally, influenza virus NP protein levels were consistently lower in the co-infection group compared to the virus-infected group (Fig. 7 d). Thus, we speculate that Lactobacillus plantarum C123 may activate additional antiviral pathways beyond the IFN axis. Lactobacillus inhibits virus replication by interfering with influenza-induced incomplete autophagy It has been known that influenza viruses employ various strategies to enhance self-replication, including the initiation of autophagy process and its subsequent block of the fusion of autophagosomes with lysosomes[ 44 – 46 ]. TBK1 is a versatile serine/threonine protein kinase with established roles in innate immunity, metabolism, autophagy, cell death, and inflammation. TBK1 within cells can be degraded through the autophagy pathway[ 47 , 48 ]. In our investigation, we find the enrichment of autophagy-related pathways in both nasal and lung microbiota functional predictions, as well as in transcriptomic KEGG enrichment analyses. Moreover, our nasal transcriptome analysis reveals heightened expression levels of pivotal autophagy-related genes. (ATG9B, GABARAPL2, MAP1LC3B/LC3B and SQSTM1/p62) in the Abx group compared to the WT group (Fig. 3 C, Cluster5, p < 0.0001, Benjamini-Hochberg). This led to us to speculate that nasal microbiota might modulate host autophagy to suppress viral replication. Therefore, we further investigated the occurrence of autophagy at the nasal infection site in the three groups. Semi-quantitative fluorescence analysis revealed that, in the Nor group, the expression of MAP1LC3B/LC3B and SQSTM1/p62 in the nasal cavity was primarily localized in basal cells; in the WT group, expression of MAP1LC3B/LC3B and SQSTM1/p62 could be detected in both ciliated epithelial cells and basal cells; in the Abx group, expression of MAP1LC3B/LC3B and SQSTM1/p62 was elevated in basal cells, accompanied by a substantial accumulation of SQSTM1/p62 in tissues (Fig. 7 A). To investigate whether cellular autophagy levels are altered in response to influenza infection (strain used in this experiment), we stably transfected influenza-infected A549 cells with the GFP-LC3B. We observed a significant increase in GFP-LC3B autophagosomes in influenza-infected cells compared to uninfected cells at 24 hours post-infection (Fig. 7 B). This elevated autophagosome count, along with blocked autophagic flux, was further confirmed by western blot analysis of endogenous lipidated and SQSTM1/p62 levels following influenza virus infection (Fig. 7 C). To further investigate whether Lactobacillus can interfere with this process, we conducted in vitro co-infection experiments of Lactobacillus plantarum C123 and influenza virus, as depicted in Fig. 6 d. The results revealed that compared to the single virus infection group, SQSTM1/p62 gradually accumulated with longer virus-exposure time, while in the co-infection group, SQSTM1/p62 levels exhibited a significant decrease at 24 hours post-infection, along with a marked reduction in intracellular NP and M1 proteins compared to the single virus infection group (Fig. 7 e). To rule out the possibility that reduced viral titers were due to a decrease in cell viability caused by Lactobacillus , we assessed cytotoxicity at 24 hours post-infection. The results showed that Lactobacillus plantarum C123 mono-infection did not induce cytotoxicity, and no significant difference in cytotoxicity was found between the single virus infection and co-infection groups (Fig. 7 F). We also stably transfected influenza-infected A549 cells with the GFP-RFP-LC3B, and observed a significant retention of green fluorescence in only influenza-infected cells compared to the co-infection cells at 24 hours post-infection (Fig. 7 G). Based on these data, we speculate that nasal microbiota is involved in the regulation of autophagy and its flux during influenza infection, reversing the inhibition of host autophagic flux induced by influenza virus and accelerating the virus clearance. 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 , 17 , 20 ]. However, most of what we know about the protection role of commensal bacteria stems from studies using mouse models, and relatively little information is available in dogs. The nasal cavity, an essential component of the upper respiratory tract, serves as the primary site for the initial contact of the influenza virus[ 49 ]. One recent study indicates a possible relationship between the microbial composition of the nasal cavity and susceptibility to the influenza virus[ 20 ]. But a clear association between nasal microbiota and susceptibility to influenza viruses, along with its underlying mechanisms, remains unclear. To investigate the potential role of nasal microbiota in the influenza virus infection, we created a model of nasal microbiota dysbiosis in three-month-old beagles by locally applying a combination of mupirocin and neomycin ointment to the nasal cavity[ 5 , 50 ]. Our data indicated that the dysregulated microbial profile in the nasal cavity displays significant variations in its contributions to functional pathways, particularly those associated with viral infection, inflammation and barrier functions, when compared to the normal microbial balance. Also, the antibiotic-treated dogs exhibited significantly exacerbated influenza infection in the respiratory tract compared with the vehicle-treated animals. Therefore, we posit that the disturbance in microbial composition of the nasal cavity may be one of the factors contributing to differential susceptibility of the host to viral infections. The analysis of microbial profiles associated with respiratory susceptibility to virus infection from two previous reports also yielded similar finding[ 20 , 51 ]. This provides us with a hint that, maintaining the stability of the respiratory ecosystem is crucial for effectively controlling viral diseases. It is known that imbalances in microbial populations can directly lead to dysfunction in mucosal barrier [ 52 ], which is closely associated with inflammation[ 53 ]. Excessive inflammation triggered by innate immune cells may result in severe multi-organ pathologies, accompanied by an excessive expression of mucosal mucins, thereby interfering with antiviral immunity[ 54 , 55 ]. In this study, we found substantial variations in microbial communities between the antibiotic-treated and the vehicle-treated groups after influenza infection. The relative abundance of Lactobacillus , Prevotella _9, Megamonas and Lachnoclostridium in both the nasal cavity and lung is significantly reduced in the Abx group, and showed a near-perfect negative correlation with that of Moraxella. We speculate that changes in the relative abundance of these bacterial genera might contribute to influenza severity. Numerous studies suggest that the symbiotic bacteria with reduced abundance mentioned above can produce a variety of short-chain fatty acids (SCFAs) during the growth process[ 56 – 58 ]. The SCFAs can stimulate the growth and/or activity of symbiotic bacteria and play a key role in maintaining health by regulating the intestinal barrier function and triggering local and systemic anti-inflammatory effects[ 59 , 60 ]. A clinical study suggests that SCFAs may provide protection against severe influenza infection by reducing tissue damage and boosting adaptive anti-viral immunity[ 61 ]. Additionally, it has been reported that Lactobacillus and Prevotella have the capability to upregulate the expression of tight junction protein 1 (TJP1/ZO-1), thereby bolstering the integrity of the epithelial barrier[ 62 , 63 ]. Our study found, following viral infection, the Abx group displayed a significantly lower expression of TJP1 compared to the WT group. Also, massive colonization by Moraxella has been demonstrated to induce a mixed proinflammatory immune response[ 64 ]. Moraxella can excessively activate the Toll-like receptor (TLR) signaling pathway, compromising the innate immune response of alveolar macrophages and contributing to exacerbations of chronic obstructive pulmonary disease (COPD) [ 65 ]. Our transcriptomic analysis of the lung 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 further inflammatory tissue damage [ 66 ]. Elevated expression of inflammatory cytokines in the Abx group has been revealed in our transcriptomic data. Additionally, previous studies have indicated that the excessive upregulation of TLR2 and TLR6 can, to a certain extent, promote the degradation of Neuropilin1 (NRP1) and Indian Hedgehog (IHH), thus reducing the expression of tight junction (TJ) proteins and ultimately weakening the epithelial barrier [ 42 ]. In agreement with this, our transcriptomic analysis demonstrated that TJ proteins, including TJP1, occludin and Cldn4, were upregulated in the Abx group. Also, we found that genes related to RhoA (Ras homolog gene family, member A) signaling pathway (Rac1, RHOA1, LIMK1 and LIMK2), linked to the destabilization of adherens junctions[ 67 ]were consistently upregulated in the Abx group. Although the relevant mechanisms underlying the influence of nasal microbiota on invading influenza virus is still unclear, these data clearly suggest that the microbiota play an important role in regulating host immunity and epithelial barrier function, thereby controlling the outcome of viral infection in the respiratory tract. Mucus plays a crucial role in safeguarding the respiratory tract against microbial infections by serving as the primary site for trapping microbes[ 67 , 68 ]. Mucus hypersecretion may result in infection and inflammation in lung injury [ 69 ]. Mucins, produced in the airway epithelia, are the main component of mucus. Therefore, maintaining mucin homeostasis is foundational to the airway health. Excessive expression of inflammatory factors such as IL1β and IL17 can stimulate the transcriptional response of MUCIN genes [ 53 ]. In the Abx group, the transcription levels of nearly all MUCIN genes, especially MUC4, MUC5B and MUC16, showed a significant upregulation compared to the WT group. Dysregulation of mucins might contribute to uncontrolled inflammation and result in abnormal airway function. Maybe significant clinical symptoms (sneezing and pulmonary crackles) and pathological alteration (interstitial pneumonia) in the Abx group could be interpreted with mucin hyperexpression. The proximity and continuity of the nasal cavity and lower respiratory tract allows the nasal microbiome to be a potential determinant of the lung microbiome [ 70 , 71 ]. In our study, the phylogenetic relationship based on the primary microbial Amplicon Sequence Variant (ASV) sequences showed a homology of 100% at genus level, including Haemophilus , Moraxella , Streptococcus , Lactobacillus , Prevotella_ 9, and Bifidobacterium between the lung and the nasal region. During the viral infection, additionally, we observed homogeneous changes in the nasal and lung microbiota. This observation provides additional evidence for the idea that a portion of the lung microbiota is derived from the nasal cavity and that a similar immune response can be induced in the nasal cavity and lung against viral infections. Notably, a previous report has indicated that H7N9 influenza virus infection leads to disruption of the respiratory microbiota and exacerbates bacterial secondary infections [ 72 ]. However, in our study, we found that infection with influenza virus (H3N2) did not significantly disrupt the nasal and lung microbiota homeostasis. We speculate that the discrepancy may result from different animal models, infection doses, sample collection method, subtypes of influenza virus and environmental factors. Similar findings have been previously reported [ 73 , 74 ]. IFNs are crucial mediator of antiviral immunity and homeostatic immune system regulation[ 75 ]. Basal levels of type I IFN production under physiological conditions are maintained by the commensal microbiota [ 76 ]. Previous studies indicate that the depletion of gut microbiota with antibiotics can reduce IFNβ-mediated antiviral activity [ 75 ]. Our study revealed a significant reduction in the transcription levels of ISGs in the nasal and lung tissues following the disruption of nasal microbiota. Therefore, we speculate that disturbance in nasal microbiota, particularly reduced relative abundance of Lactobacillus , may attenuate the host's IFN-mediated antiviral immune response. A recent study has demonstrated that Lactobacillus paracasei modulates lung immunity and enhances the ability to combat influenza virus infection [ 77 ]. Notably, in our study, disruption of nasal microbiota was observed to exacerbate dysbiosis in lung microbiota, followed by increased severity of influenza infection. The Abx group exhibited low levels of ISGs transcription and high levels of inflammatory factor transcription in both nasal and lung compartments post-infection. However, comparison between the Abx and WT groups revealed distinctive transcription patterns of ISGs and inflammatory factors in both nasal and lung tissues. In the nasal compartment, the WT group demonstrated higher levels of ISGs transcription compared to the Abx group, while in the lung tissue, the Abx group exhibited superior transcription levels in inflammatory factors. We speculate that these discrepancies may be attributable to variations in cellular distribution and composition across the two tissues, as well as differences in the composition and relative abundance of nasal and lung microbiota. In future studies, we will employ single-cell sequencing combined with metagenomic analyses to further elucidate the underlying biological disparities. Finally, we isolated ten strains of lactic acid bacteria from the experimental dogs and identified one strain of Lactobacillus with in vitro antiviral activity. This antiviral effect was mediated by the activation of the IFN pathway and the modulation of influenza-induced autophagy inhibition, with cross-talk observed between these two mechanisms. However, the exact mechanisms by which Lactobacillus regulates the interplay between IFN activation and autophagy remain unclear. We hypothesize that Lactobacillus may produce certain substances during the growth, such as short-chain fatty acids (SCFAs), which have been reported to play significant roles in regulating host autophagy and innate immunity[ 78 , 79 ]. Additionally, type I IFNs have been reported to induce autophagy and enhance autophagic flux in human cancer cell lines[ 80 ]. Further research is needed to elucidate the underlying mechanisms. 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, regarding lung sample collection for sequencing, the samples were obtained from multiple mixed tissue within individuals' lungs. It is likely that microbiome heterogeneity (and thus pneumotype categorization) varies over time and across different segments of the lung. Furthermore, the presence of different cell types across tissues contributes to varied and complex immune responses, and transcriptomic analysis may not accurately reflect the immune reactions of distinct ecological niches. Thirdly, the use of cell models may be insufficient to capture the complexity of the in vivo environment. Therefore, our team is presently enhancing sequencing methodologies and experimental designs to delve into the specific mechanisms and potential biological significance of these findings. 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. Conclusions Collectively, our results indicate that nasal dysbiosis exacerbates microbial dysbiosis of the lungs following viral infection, further diminishing the host's intrinsic antiviral response, amplifying the generation of inflammatory storms, and compromising respiratory barrier integrity. Intriguingly, our study revealed that Lactobacillus and Moraxella might exert completely opposing effects in modulating innate immunity and inflammatory responses. Further study will focus on the precise functions of specific bacterial genus or species and the underlying mechanisms. Declarations Ethics approval and consent to participate 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. Consent for publication Not applicable. 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). Availability of data and materials All raw data used in this study are available in the NCBI Sequence Read Archive (SRA), accession numbers PRJNA1125627, PRJNA1126008.Consent for publication Competing interests Authors declare that they have no competing interests. Authors’ 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. Acknowledgements Not applicable. References de Steenhuijsen Piters WAA, Watson RL, de Koff EM, Hasrat R, Arp K, Chu M, et al. Early-life viral infections are associated with disadvantageous immune and microbiota profiles and recurrent respiratory infections. Nat Microbiol. 2022;7(2):224–37; doi: 10.1038/s41564-021-01043-2 . Man WH, van Houten MA, Merelle ME, Vlieger AM, Chu M, Jansen NJG, et al. Bacterial and viral respiratory tract microbiota and host characteristics in children with lower respiratory tract infections: a matched case-control study. Lancet Respir Med. 2019;7(5):417–26; doi: 10.1016/S2213-2600(18)30449-1 . Bosch A, de Steenhuijsen Piters WAA, van Houten MA, Chu M, Biesbroek G, Kool J, et al. Maturation of the Infant Respiratory Microbiota, Environmental Drivers, and Health Consequences. A Prospective Cohort Study. Am J Respir Crit Care Med. 2017;196(12):1582–90; doi: 10.1164/rccm.201703-0554OC . Lloyd CM, Saglani S. Early-life respiratory infections and developmental immunity determine lifelong lung health. Nat Immunol. 2023;24(8):1234–43; doi: 10.1038/s41590-023-01550-w . Krismer B, Weidenmaier C, Zipperer A, Peschel A. The commensal lifestyle of Staphylococcus aureus and its interactions with the nasal microbiota. Nat Rev Microbiol. 2017;15(11):675–87; doi: 10.1038/nrmicro.2017.104 . Caballero-Flores G, Pickard JM, Nunez G. Microbiota-mediated colonization resistance: mechanisms and regulation. Nat Rev Microbiol. 2023;21(6):347–60; doi: 10.1038/s41579-022-00833-7 . Tan AH, Lim SY, Lang AE. The microbiome-gut-brain axis in Parkinson disease - from basic research to the clinic. Nat Rev Neurol. 2022;18(8):476–95; doi: 10.1038/s41582-022-00681-2 . Hsu CL, Schnabl B. The gut-liver axis and gut microbiota in health and liver disease. Nat Rev Microbiol. 2023;21(11):719–33; doi: 10.1038/s41579-023-00904-3 . Zhang F, Lau RI, Liu Q, Su Q, Chan FKL, Ng SC. Gut microbiota in COVID-19: key microbial changes, potential mechanisms and clinical applications. Nat Rev Gastroenterol Hepatol. 2023;20(5):323–37; doi: 10.1038/s41575-022-00698-4 . Zhang J, Wu Y, Liu J, Yang Y, Li H, Wu X, et al. Differential Oral Microbial Input Determines Two Microbiota Pneumo-Types Associated with Health Status. Adv Sci (Weinh). 2022;9(32):e2203115; doi: 10.1002/advs.202203115 . Natalini JG, Singh S, Segal LN. The dynamic lung microbiome in health and disease. Nat Rev Microbiol. 2023;21(4):222–35; doi: 10.1038/s41579-022-00821-x . Wypych TP, Wickramasinghe LC, Marsland BJ. The influence of the microbiome on respiratory health. Nat Immunol. 2019;20(10):1279–90; doi: 10.1038/s41590-019-0451-9 . Liu W, Xu J, Pi Z, Chen Y, Jiang G, Wan Y, et al. Untangling the web of intratumor microbiota in lung cancer. Biochim Biophys Acta Rev Cancer. 2023;1878(6):189025; doi: 10.1016/j.bbcan.2023.189025 . Proctor DM, Relman DA. The Landscape Ecology and Microbiota of the Human Nose, Mouth, and Throat. Cell Host Microbe. 2017;21(4):421–32; doi: 10.1016/j.chom.2017.03.011 . Liang W, Yang Y, Gong S, Wei M, Ma Y, Feng R, et al. Airway dysbiosis accelerates lung function decline in chronic obstructive pulmonary disease. Cell Host Microbe . 2023;31(6):1054-70 e9; doi: 10.1016/j.chom.2023.04.018 . Wu BG, Sulaiman I, Tsay JJ, Perez L, Franca B, Li Y, et al. Episodic Aspiration with Oral Commensals Induces a MyD88-dependent, Pulmonary T-Helper Cell Type 17 Response that Mitigates Susceptibility to Streptococcus pneumoniae. Am J Respir Crit Care Med. 2021;203(9):1099–111; doi: 10.1164/rccm.202005-1596OC . Horn KJ, Schopper MA, Drigot ZG, Clark SE. Airway Prevotella promote TLR2-dependent neutrophil activation and rapid clearance of Streptococcus pneumoniae from the lung. Nat Commun. 2022;13(1):3321; doi: 10.1038/s41467-022-31074-0 . Rosas-Salazar C, Kimura KS, Shilts MH, Strickland BA, Freeman MH, Wessinger BC, et al. SARS-CoV-2 infection and viral load are associated with the upper respiratory tract microbiome. J Allergy Clin Immunol . 2021;147(4):1226-33 e2; doi: 10.1016/j.jaci.2021.02.001 . Ederveen THA, Ferwerda G, Ahout IM, Vissers M, de Groot R, Boekhorst J, et al. Haemophilus is overrepresented in the nasopharynx of infants hospitalized with RSV infection and associated with increased viral load and enhanced mucosal CXCL8 responses. Microbiome. 2018;6(1):10; doi: 10.1186/s40168-017-0395-y . Lee KH, Gordon A, Shedden K, Kuan G, Ng S, Balmaseda A, et al. The respiratory microbiome and susceptibility to influenza virus infection. PLoS One. 2019;14(1):e0207898; doi: 10.1371/journal.pone.0207898 . Groeger D, Schiavi E, Grant R, Kurnik-Lucka M, Michalovich D, Williamson R, et al. Intranasal Bifidobacterium longum protects against viral-induced lung inflammation and injury in a murine model of lethal influenza infection. EBioMedicine. 2020;60:102981; doi: 10.1016/j.ebiom.2020.102981 . Lin Y, Xie X, Zhao Y, Kalhoro DH, Lu C, Liu Y. Enhanced replication of avian-origin H3N2 canine influenza virus in eggs, cell cultures and mice by a two-amino acid insertion in neuraminidase stalk. Vet Res. 2016;47(1):53; doi: 10.1186/s13567-016-0337-x . Kang YM, Kim HM, Ku KB, Park EH, Yum J, Seo SH. H3N2 canine influenza virus causes severe morbidity in dogs with induction of genes related to inflammation and apoptosis. Vet Res. 2013;44(1):92; doi: 10.1186/1297-9716-44-92 . Cuthbertson L, Craven V, Bingle L, Cookson W, Everard ML, Moffatt MF. The impact of persistent bacterial bronchitis on the pulmonary microbiome of children. PLoS One. 2017;12(12):e0190075; doi: 10.1371/journal.pone.0190075 . Habibi MS, Thwaites RS, Chang M, Jozwik A, Paras A, Kirsebom F, et al. Neutrophilic inflammation in the respiratory mucosa predisposes to RSV infection. Science. 2020;370(6513); doi: 10.1126/science.aba9301 . Liu YX, Qin Y, Chen T, Lu M, Qian X, Guo X, et al. A practical guide to amplicon and metagenomic analysis of microbiome data. Protein Cell. 2021;12(5):315–30; doi: 10.1007/s13238-020-00724-8 . Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26(19):2460–1; doi: 10.1093/bioinformatics/btq461 . Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584; doi: 10.7717/peerj.2584 . Oksanen J, Kindt R, Legendre P, O’Hara B, Stevens MHH, Oksanen MJ, et al. The vegan package. Community ecology package. 2007;10(631–637):719. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550; doi: 10.1186/s13059-014-0550-8 . Snow GL. ggplot2: Elegant Graphics for Data Analysis. The American statistician. 2011;(3):65. Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, et al. Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012;9(7):676–82; doi: 10.1038/nmeth.2019 . Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics. 2012;16(5):284–7; doi: 10.1089/omi.2011.0118 . Quadros RM, Miura H, Harms DW, Akatsuka H, Sato T, Aida T, et al. Easi-CRISPR: a robust method for one-step generation of mice carrying conditional and insertion alleles using long ssDNA donors and CRISPR ribonucleoproteins. Genome Biol. 2017;18(1):92; doi: 10.1186/s13059-017-1220-4 . Zhu W, Winter MG, Byndloss MX, Spiga L, Duerkop BA, Hughes ER, et al. Precision editing of the gut microbiota ameliorates colitis. Nature. 2018;553(7687):208–11; doi: 10.1038/nature25172 . Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR, Taylor CM, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020;38(6):685–88; doi: 10.1038/s41587-020-0548-6 . Powers JH, 3rd, Bacci ED, Guerrero ML, Leidy NK, Stringer S, Kim K, et al. Reliability, Validity, and Responsiveness of InFLUenza Patient-Reported Outcome (FLU-PRO©) Scores in Influenza-Positive Patients. Value Health. 2018;21(2):210–18; doi: 10.1016/j.jval.2017.04.014 . de Jonge J, Isakova-Sivak I, van Dijken H, Spijkers S, Mouthaan J, de Jong R, et al. H7N9 Live Attenuated Influenza Vaccine Is Highly Immunogenic, Prevents Virus Replication, and Protects Against Severe Bronchopneumonia in Ferrets. Mol Ther. 2016;24(5):991–1002; doi: 10.1038/mt.2016.23 . de Steenhuijsen Piters WAA, Binkowska J, Bogaert D. Early Life Microbiota and Respiratory Tract Infections. Cell Host Microbe. 2020;28(2):223–32; doi: 10.1016/j.chom.2020.07.004 . Chatterjee M, van Putten JPM, Strijbis K. Defensive Properties of Mucin Glycoproteins during Respiratory Infections-Relevance for SARS-CoV-2. mBio. 2020;11(6); doi: 10.1128/mBio.02374-20 . Radicioni G, Ceppe A, Ford AA, Alexis NE, Barr RG, Bleecker ER, et al. Airway mucin MUC5AC and MUC5B concentrations and the initiation and progression of chronic obstructive pulmonary disease: an analysis of the SPIROMICS cohort. Lancet Respir Med. 2021;9(11):1241–54; doi: 10.1016/S2213-2600(21)00079-5 . Pontarollo G, Kollar B, Mann A, Khuu MP, Kiouptsi K, Bayer F, et al. Commensal bacteria weaken the intestinal barrier by suppressing epithelial neuropilin-1 and Hedgehog signaling. Nat Metab. 2023;5(7):1174–87; doi: 10.1038/s42255-023-00828-5 . Zhang H, Han C, Li T, Li N, Cao X. The methyltransferase PRMT6 attenuates antiviral innate immunity by blocking TBK1-IRF3 signaling. Cell Mol Immunol. 2019;16(10):800–09; doi: 10.1038/s41423-018-0057-4 . Guo X, Zhang Z, Lin C, Ren H, Li Y, Zhang Y, et al. A/(H1N1) pdm09 NS1 promotes viral replication by enhancing autophagy through hijacking the IAV negative regulatory factor LRPPRC. Autophagy. 2023;19(5):1533–50; doi: 10.1080/15548627.2022.2139922 . Beale R, Wise H, Stuart A, Ravenhill BJ, Digard P, Randow F. A LC3-interacting motif in the influenza A virus M2 protein is required to subvert autophagy and maintain virion stability. Cell Host Microbe. 2014;15(2):239–47; doi: 10.1016/j.chom.2014.01.006 . Munz C. Influenza A virus lures autophagic protein LC3 to budding sites. Cell Host Microbe. 2014;15(2):130–1; doi: 10.1016/j.chom.2014.01.014 . Miranda A, Shirley CA, Jenkins RW. Emerging roles of TBK1 in cancer immunobiology. Trends Cancer. 2024; doi: 10.1016/j.trecan.2024.02.007 . Hu Z, Xie Y, Lu J, Yang J, Zhang J, Jiang H, et al. VANGL2 inhibits antiviral IFN-I signaling by targeting TBK1 for autophagic degradation. Sci Adv. 2023;9(25):eadg2339; doi: 10.1126/sciadv.adg2339 . Gamblin SJ, Skehel JJ. Influenza hemagglutinin and neuraminidase membrane glycoproteins. J Biol Chem. 2010;285(37):28403–9; doi: 10.1074/jbc.R110.129809 . Pillarisetti N, Broderick D, Ainsworth A, Mulholland A, Wagner Mackenzie B, Middleton D, et al. The airway microbiota in children newly diagnosed with bronchiectasis largely retains its diversity. Eur Respir J. 2019;54(2); doi: 10.1183/13993003.00704-2019 . Kim JG, Zhang A, Rauseo AM, Goss CW, Mudd PA, O'Halloran JA, et al. The salivary and nasopharyngeal microbiomes are associated with SARS-CoV-2 infection and disease severity. J Med Virol. 2023;95(2):e28445; doi: 10.1002/jmv.28445 . Zhao X, Jiang L, Fang X, Guo Z, Wang X, Shi B, et al. Host-microbiota interaction-mediated resistance to inflammatory bowel disease in pigs. Microbiome. 2022;10(1):115; doi: 10.1186/s40168-022-01303-1 . Tan HT, Hagner S, Ruchti F, Radzikowska U, Tan G, Altunbulakli C, et al. Tight junction, mucin, and inflammasome-related molecules are differentially expressed in eosinophilic, mixed, and neutrophilic experimental asthma in mice. Allergy. 2019;74(2):294–307; doi: 10.1111/all.13619 . Costain G, Liu Z, Mennella V, Radicioni G, Goczi AN, Albulescu A, et al. Hereditary Mucin Deficiency Caused by Biallelic Loss of Function of MUC5B. Am J Respir Crit Care Med. 2022;205(7):761–68; doi: 10.1164/rccm.202106-1456OC . Huang B. Mucins produced by type II pneumocyte: culprits in SARS-CoV-2 pathogenesis. Cell Mol Immunol. 2021;18(7):1823–25; doi: 10.1038/s41423-021-00714-8 . Zhu H, Cao C, Wu Z, Zhang H, Sun Z, Wang M, et al. The probiotic L. casei Zhang slows the progression of acute and chronic kidney disease. Cell Metab . 2021;33(10):1926-42 e8; doi: 10.1016/j.cmet.2021.06.014 . Chen Y, Liu Y, Wang Y, Chen X, Wang C, Chen X, et al. Prevotellaceae produces butyrate to alleviate PD-1/PD-L1 inhibitor-related cardiotoxicity via PPARalpha-CYP4X1 axis in colonic macrophages. J Exp Clin Cancer Res. 2022;41(1):1; doi: 10.1186/s13046-021-02201-4 . Jing Y, Yang D, Bai F, Wang Q, Zhang C, Yan Y, et al. Spinal cord injury-induced gut dysbiosis influences neurological recovery partly through short-chain fatty acids. NPJ Biofilms Microbiomes. 2023;9(1):99; doi: 10.1038/s41522-023-00466-5 . Deleu S, Machiels K, Raes J, Verbeke K, Vermeire S. Short chain fatty acids and its producing organisms: An overlooked therapy for IBD? EBioMedicine. 2021;66:103293; doi: 10.1016/j.ebiom.2021.103293 . Wang RX, Lee JS, Campbell EL, Colgan SP. Microbiota-derived butyrate dynamically regulates intestinal homeostasis through regulation of actin-associated protein synaptopodin. Proc Natl Acad Sci U S A. 2020;117(21):11648–57; doi: 10.1073/pnas.1917597117 . Trompette A, Gollwitzer ES, Pattaroni C, Lopez-Mejia IC, Riva E, Pernot J, et al. Dietary Fiber Confers Protection against Flu by Shaping Ly6c(-) Patrolling Monocyte Hematopoiesis and CD8(+) T Cell Metabolism. Immunity. 2018;48(5):992–1005.e8; doi: 10.1016/j.immuni.2018.04.022 . Heeney DD, Zhai Z, Bendiks Z, Barouei J, Martinic A, Slupsky C, et al. Lactobacillus plantarum bacteriocin is associated with intestinal and systemic improvements in diet-induced obese mice and maintains epithelial barrier integrity in vitro. Gut Microbes. 2019;10(3):382–97; doi: 10.1080/19490976.2018.1534513 . Ramsheh MY, Haldar K, Esteve-Codina A, Purser LF, Richardson M, Müller-Quernheim J, et al. Lung microbiome composition and bronchial epithelial gene expression in patients with COPD versus healthy individuals: a bacterial 16S rRNA gene sequencing and host transcriptomic analysis. Lancet Microbe. 2021;2(7):e300-e10; doi: 10.1016/s2666-5247(21)00035-5 . Sulaiman I, Wu BG, Chung M, Isaacs B, Tsay JJ, Holub M, et al. Lower Airway Dysbiosis Augments Lung Inflammatory Injury in Mild-to-Moderate Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med. 2023;208(10):1101–14; doi: 10.1164/rccm.202210-1865OC . Slevogt H, Seybold J, Tiwari KN, Hocke AC, Jonatat C, Dietel S, et al. Moraxella catarrhalis is internalized in respiratory epithelial cells by a trigger-like mechanism and initiates a TLR2- and partly NOD1-dependent inflammatory immune response. Cell Microbiol. 2007;9(3):694–707; doi: 10.1111/j.1462-5822.2006.00821.x . Bezemer GF, Sagar S, van Bergenhenegouwen J, Georgiou NA, Garssen J, Kraneveld AD, et al. Dual role of Toll-like receptors in asthma and chronic obstructive pulmonary disease. Pharmacol Rev. 2012;64(2):337–58; doi: 10.1124/pr.111.004622 . Komarova YA, Kruse K, Mehta D, Malik AB. Protein Interactions at Endothelial Junctions and Signaling Mechanisms Regulating Endothelial Permeability. Circ Res. 2017;120(1):179–206; doi: 10.1161/circresaha.116.306534 . Zanin M, Baviskar P, Webster R, Webby R. The Interaction between Respiratory Pathogens and Mucus. Cell Host Microbe. 2016;19(2):159–68; doi: 10.1016/j.chom.2016.01.001 . Rogers DF. Physiology of airway mucus secretion and pathophysiology of hypersecretion. Respir Care. 2007;52(9):1134–46; discussion 46 – 9. Bassis CM, Erb-Downward JR, Dickson RP, Freeman CM, Schmidt TM, Young VB, et al. Analysis of the upper respiratory tract microbiotas as the source of the lung and gastric microbiotas in healthy individuals. mBio. 2015;6(2):e00037; doi: 10.1128/mBio.00037-15 . Dickson RP, Erb-Downward JR, Huffnagle GB. Homeostasis and its disruption in the lung microbiome. Am J Physiol Lung Cell Mol Physiol. 2015;309(10):L1047-55; doi: 10.1152/ajplung.00279.2015 . Ventero MP, Moreno-Perez O, Molina-Pardines C, Paytuví-Gallart A, Boix V, Escribano I, et al. Nasopharyngeal Microbiota as an early severity biomarker in COVID-19 hospitalised patients. J Infect. 2022;84(3):329–36; doi: 10.1016/j.jinf.2021.12.030 . Hau SJ, Nielsen DW, Mou KT, Alt DP, Kellner S, Brockmeier SL. Resilience of swine nasal microbiota to influenza A virus challenge in a longitudinal study. Vet Res. 2023;54(1):38; doi: 10.1186/s13567-023-01167-9 . Ding T, Song T, Zhou B, Geber A, Ma Y, Zhang L, et al. Microbial Composition of the Human Nasopharynx Varies According to Influenza Virus Type and Vaccination Status. mBio. 2019;10(4); doi: 10.1128/mBio.01296-19 . Stefan KL, Kim MV, Iwasaki A, Kasper DL. Commensal Microbiota Modulation of Natural Resistance to Virus Infection. Cell. 2020;183(5):1312-24.e10; doi: 10.1016/j.cell.2020.10.047 . Abt MC, Osborne LC, Monticelli LA, Doering TA, Alenghat T, Sonnenberg GF, et al. Commensal bacteria calibrate the activation threshold of innate antiviral immunity. Immunity. 2012;37(1):158–70; doi: 10.1016/j.immuni.2012.04.011 . Kim S, Lee S, Kim TY, Lee SH, Seo SU, Kweon MN. Newly isolated Lactobacillus paracasei strain modulates lung immunity and improves the capacity to cope with influenza virus infection. Microbiome. 2023;11(1):260; doi: 10.1186/s40168-023-01687-8 . Tang Y, Chen Y, Jiang H, Nie D. Short-chain fatty acids induced autophagy serves as an adaptive strategy for retarding mitochondria-mediated apoptotic cell death. Cell Death Differ. 2011;18(4):602–18; doi: 10.1038/cdd.2010.117 . Wang G, Liu J, Zhang Y, Xie J, Chen S, Shi Y, et al. Ginsenoside Rg3 enriches SCFA-producing commensal bacteria to confer protection against enteric viral infection via the cGAS-STING-type I IFN axis. ISME J. 2023;17(12):2426–40; doi: 10.1038/s41396-023-01541-7 . Ambjorn M, Ejlerskov P, Liu Y, Lees M, Jaattela M, Issazadeh-Navikas S. IFNB1/interferon-beta-induced autophagy in MCF-7 breast cancer cells counteracts its proapoptotic function. Autophagy. 2013;9(3):287–302; doi: 10.4161/auto.22831 . Additional Declarations No competing interests reported. Supplementary Files Microbiomesupplementary.docx Cite Share Download PDF Status: Published Journal Publication published 27 Jan, 2025 Read the published version in Microbiome → Version 1 posted Editorial decision: Revision requested 01 Jul, 2024 Editor assigned by journal 01 Jul, 2024 Submission checks completed at journal 24 Jun, 2024 First submitted to journal 20 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4612057","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":321221357,"identity":"a5c2102c-8a94-4ae1-8625-cecb06ca20f2","order_by":0,"name":"Jinzhu Geng","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Jinzhu","middleName":"","lastName":"Geng","suffix":""},{"id":321221360,"identity":"a0241d2f-00e6-4a0b-810b-3d9a2d133bdc","order_by":1,"name":"Yuhao Dong","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yuhao","middleName":"","lastName":"Dong","suffix":""},{"id":321221363,"identity":"1a9b2b4d-2dc8-44c2-ae33-6fdaa0bfb460","order_by":2,"name":"Hao Huang","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Huang","suffix":""},{"id":321221365,"identity":"3cfce850-665d-467a-afe0-5e205d45127e","order_by":3,"name":"Xia Wen","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Wen","suffix":""},{"id":321221366,"identity":"894b0ccd-21dc-4a18-885f-6c7d85b83115","order_by":4,"name":"Ting Xu","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Xu","suffix":""},{"id":321221368,"identity":"cca87cc8-1f36-4de0-a4fd-b054afd71ddb","order_by":5,"name":"Yanbing Zhao","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yanbing","middleName":"","lastName":"Zhao","suffix":""},{"id":321221369,"identity":"dfd27cb5-b958-45c9-8ab6-0ca66b04b55e","order_by":6,"name":"Yongjie Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIie2RsQrCMBBAr0TSJbbrFUF/IVNRBP0VoaCLguCqHeOi7v0MF3WsBHSpu6NTJwddBBcxVcEt7SiYt+QN97iEABgMP4hD3iehQGLAl3f0Cf0mtFMw+SrjH8lLbObjdTOxHXt2O9XHEly7z+G+0V2M+V6U7AllhzXHnQRvdubWPNEnlbLYEYqDFSKVwI99TixRJKmdU8SHhHbBZKy2MIqeUFswN6GjRiRi9Zauj96ixzBJh9u5JnFduTxeRRjUpjKt4K1ZdafB8nTXJIqS+kAZZEaUsUxibaAGLwBhKzPrkjNqMBgM/8kTYXxBc/sQZucAAAAASUVORK5CYII=","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Yongjie","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-06-20 13:23:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4612057/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4612057/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40168-025-02031-y","type":"published","date":"2025-01-27T15:57:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60416011,"identity":"47f08315-b725-4d88-a142-8de65e8fd541","added_by":"auto","created_at":"2024-07-16 13:54:15","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2759038,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDysbiosis of the nasal microbiome exacerbates influenza-induced respiratory disease.\u003c/strong\u003e (A)Boxplot showing the Richness and Chao diversity index of nasal microbiota in dogs with Before and Clean. The data were analyzed by a two-way ANOVA test, Tukey's HSD. (B) Principal coordinate analysis (PCoA) of microbiota communities utilizing Bray-Curtis distances for samples before and after antibiotic treatment. below and left boxplots show the overall distribution of PCoA 1 and PCoA 2 scores in each group. (C) A heatmap illustrating the differential relative contribution of KEGG pathways, predicted based on the microbiota composition of Before and Clean. (D) Flowchart overview of the study design. (E, F) Changes in rectal temperature and body weight after intranasal challenge with influenza virus. Rectal temperature and body weight data were presented as mean ± SEM, and a two-way ANOVA test was used for analysis. * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01, and *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 indicate a significant difference between the WT and Abx groups. # \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ## \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, and ### \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001 indicate a significant difference between the Nor and WT groups. (G) Disease severity scores of dogs infected with influenza virus at post-infection (dpi) in the Nor, WT and Abx groups. (H) Virus titer in nasal swabs and lungs following intranasal challenge with influenza virus. (I) Gross anatomy of the lung and histopathological appearance of H\u0026amp;E-stained nasal, tracheal, and lung tissues from the dogs infected with influenza virus at 8 dpi. Both the WT and Abx groups exhibited interstitial pneumonia, characterized by the thickening of alveolar septa due to the infiltration of numerous inflammatory cells. (J) Histological scores of nasal, tracheal, and lung tissues infected with influenza virus at 8 dpi in the Nor, WT and Abx groups. (K, I) Immunofluorescent staining of lung tissues infected with influenza virus at 8 dpi.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4612057/v1/a01add590e5c6bc4b275eb7a.jpg"},{"id":60415210,"identity":"a856c6bd-45fb-437c-b7e2-ccbb4852ae8f","added_by":"auto","created_at":"2024-07-16 13:46:15","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2330875,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential bacterial composition analysis and functional prediction of microbia in the nasal cavity.\u003c/strong\u003e (A) The ternary plot illustrates the relative enriched genera after infection with influenza virus. Each dot represents a bacterial genus, colored according to the phylum in which it is most highly abundant. The size of the dot is proportional to its relative abundance, and its position is determined based on the contribution of each subgroup to the overall abundance. (B) A boxplot depicting the differential relative abundance of ASVs after infection with influenza virus. * \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, and *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, indicate significant differences between groups (one-way ANOVA test, Tukey's HSD). (C) Pearson correlations were computed for the differential relative abundance of ASVs within the WT and Abx groups. Significance was visually represented by squares, with the coloration of squares and interconnecting lines denoting the directionality of the correlation, either positive or negative. (D) The Pearson correlations were calculated between the differential relative abundance of ASVs in the WT and Abx groups and the nasal or lung virus titer. * \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, and *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, indicate significant differences between the groups (two-way ANOVA test, Tukey's HSD). (E) A heatmap illustrates the distinct relative contributions of KEGG pathways, predicted based on the microbiota composition of Nor (Control group post influenza infection), WT (influenza -infected group), and Abx (influenza -infected group post antibiotic treatment), utilizing PICRUSt2. Significance levels, generated by the DESeq2 package, are denoted as follows: * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003e p\u003c/em\u003e\u0026lt; 0.01, and *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, indicating significant differences between groups (Benjamini-Hochberg).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4612057/v1/78b270189c0b023554eff543.jpg"},{"id":60415213,"identity":"5cdcd43c-1f86-4e1b-850c-43a28d281c04","added_by":"auto","created_at":"2024-07-16 13:46:16","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3157052,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDysbiosis of nasal microbiome diminishes the antiviral response within the nasal cavity.\u003c/strong\u003e(A)Volcano plot of differentially expressed genes between Nor and WT, Nor and Abx, Abx and WT.(B) The Venn diagram showed the shared and unique genes of DEGs were statistically analyzed.(C)Performing clustering analysis on differential genes in nasal tissue from three groups using the Fuzzy-c means (FCM) algorithm.(D) The k-means clustering analysis of the DEGs was conducted using the STING online database (https://string-db.org/, Accessed 20 May 2023) with a minimum required interaction score of 0.4. The DEGs in the nasal tissue between the WT and Abx groups were primarily categorized into five clusters. These clusters predominantly encompass the genes associated with IFN-mediated antiviral responses (Cluster 1), mucin generation (Cluster 2), barrier formation (Cluster 3), inflammation (Cluster 4) and autophagy pathway (Clusters5). (E) The heatmap illustrates the DEGs among five clusters between the WT and Abx groups. All genes were normalized based on the corresponding genes in the Nor group before conducting differential analysis. All corresponding significance levels are determined by the DESeq2 package (Benjamini-Hochberg). (F) Transcription levels of cellular factors in blood after viral infection among Nor, WT, and Abx groups. Scatter plots illustrate the transcription levels of cellular factors IFNβ1, ISG15, Mx1, OAS1, IL1, IL6, TNFα, and caspase3 in the blood on the eighth day after viral infection for the Nor group, WT group, and Abx group (one-way ANOVA test, Tukey's HSD). (G) Immunofluorescence staining of the nasal tissue, with ZO-1/TJP1 protein stained in red and cell nuclei in blue (DAPI). ZO-1/TJP1 exhibited uniform expression in the Nor group, but presented heightened expression at the injury site in the WT group (indicated by an arrow). In contrast, the Abx group displayed an overall diminished expression of ZO-1/TJP1 protein at the injury site.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4612057/v1/5fc1ff48a937dec00d95b8e6.jpg"},{"id":60415216,"identity":"27c27205-4540-4281-9e8d-2345f1ece163","added_by":"auto","created_at":"2024-07-16 13:46:16","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1917423,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDisruption of nasal microbiota exacerbates the dysbiosis of lung microbiota following influenza infection.\u003c/strong\u003e (A) Experimental scheme of lung tissue sampling and analysis. The lung of each individual was sampled at six target points and then mixed on an individual basis for further analysis. (B, C) The bacterial composition at the phylum and genus levels in canine lung tissue. (D) The volcano plot illustrates differentially abundant microbial taxa at the genus level between the Abx and WT groups. (E) Boxplot showing the Richness and Chao1 diversity indices of lung microbiota in the Nor, WT, and Abx groups (one-way ANOVA test, Tukey's HSD). (F) Principal Coordinate Analysis (PCoA) of microbiota communities employing Bray–Curtis distances for lung microbiota samples in the Nor, WT, and Abx groups after influenza infection. Boxplots below and to the left illustrate the comprehensive distribution of PCoA 1 and PCoA 2 scores within each group (Wilcoxon rank-sum test). (G) The volcano plot depicts the differential contribution of KEGG pathways, forecasted from the lung microbial composition of the Abx and WT groups by PICRUSt2. differential analysis was conducted using the DESeq2 package (Benjamini-Hochberg). (H) The Pearson correlations were calculated between differential relative abundance of ASVs and contribution of KEGG pathways based on lung microbial composition in the WT and Abx groups.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4612057/v1/faebbc89120f5951e2419af7.jpg"},{"id":60415214,"identity":"83aa1d99-0466-4eb3-8410-0e573e02076d","added_by":"auto","created_at":"2024-07-16 13:46:16","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2494316,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDisruption of lung microbiota exacerbates inflammatory response and barrier damage following influenza infection.\u003c/strong\u003e (A)The k-means clustering analysis of the DEGs in the lung was conducted using the STING online database with a minimum required interaction score of 0.4. The lung DEGs between the WT and Abx groups were primarily categorized into four clusters. (B)The heatmap illustrates the DEGs among four clusters between the WT and Abx groups. All genes were normalized based on the corresponding genes in the Nor group before conducting differential analysis. All significance levels are determined by the DESeq2 package (Benjamini-Hochberg). (C)The heatmap depicting the transcription levels of genes associated with the inflammation (IL6 and TNFα), apoptosis (Bax, and Caspase3), and antiviral response (IFNβ1, IFNα, OAS1, Mx1, PKR, ISG15, Myd88, and Mx2) in lung tissues, determined by RT-qPCR. (D)Correlation analysis between the differentially abundant ASVs at the genus level and the DEGs in the WT and Abx groups through the Mantel test. (E) Protein expression of NP, TBK1, IRF3 and NF-κB/p65, and phosphorylation of TBK1, IRF3 and NF-κB/p65 in lung tissues were assessed by Western blot analysis. GAPDH was employed as an internal reference. Grayscale analysis was performed using ImageJ software. (F) Pearson correlations between the differentially relative abundance of A88 (\u003cem\u003eMoraxella\u003c/em\u003e) and A15 (\u003cem\u003eLactobacillus\u003c/em\u003e) and the transcriptional levels of IFNβ1, Myd88, Caspase3 and TNFα in the lung tissues within the WT and Abx groups.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4612057/v1/07513a09dd4b9a9fe24a32a2.jpg"},{"id":60415215,"identity":"adb51fce-cc83-4d3b-9ecf-4b140a5395e3","added_by":"auto","created_at":"2024-07-16 13:46:16","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1475075,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eLactobacillus\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eexert antiviral effects in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003evitro\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e by activating IFN-mediated pathways. (A)\u003c/strong\u003e Experimental workflow for in \u003cem\u003evitro\u003c/em\u003eantiviral activity assessment of lactic acid bacteria. \u003cstrong\u003e(B)\u003c/strong\u003eWestern blot and CCK-8 analysis to evaluate the in \u003cem\u003evitro\u003c/em\u003e antiviral effects and cytotoxicity of canine-derived lactic acid bacteria isolates.\u003cstrong\u003e (C) \u003c/strong\u003eDual-luciferase promoter activity assay to assess IFN-β promoter activity and NF-κB/p65 pathway activation during co-infection with lactic acid bacteria and influenza virus.\u003cstrong\u003e (D) \u003c/strong\u003eWestern blot analysis to detect total and phosphorylated TBK1 levels, evaluating IFN pathway activation during infection with \u003cem\u003eLactobacillus. plantarum\u003c/em\u003eand influenza virus.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4612057/v1/94888c1d9ae2c182f2464573.jpg"},{"id":60415218,"identity":"08e1c7ce-dc97-4d5e-940d-3a8eb264150e","added_by":"auto","created_at":"2024-07-16 13:46:16","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3037636,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eLactobacillus\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eplantarum interferes with influenza-mediated incomplete autophagy to suppress replication in A549 cells. (A)\u003c/strong\u003eImmunofluorescence staining of the nasal tissue, with MAP1LC3B/LC3B protein stained in red, SQSTM1/p62 protein in green and cell nuclei in blue (DAPI). The co-localization of MAP1LC3B/LC3B and SQSTM1/p62 proteins was consistently observed across all experimental cohorts.\u003cstrong\u003e (B) \u003c/strong\u003eWestern blot analysis was employed to assess the lipidation status of LC3B (LC3B II) and the accumulation of SQSTM1/p62 protein subsequent to influenza infection. LC3B lipidation exhibited a progressive increase in influenza-infected A549 cells throughout the infection duration, concomitant with the continuous accumulation of SQSTM1/p62 protein. This underscores the capacity of influenza infection to induce autophagy in A549 cells while potentially impeding the regular course of autophagic flux. \u003cstrong\u003e(C) \u003c/strong\u003eFluorescence micrographs display A549 cells expressing GFP-RFP-LC3B, subjected to treatment with DMSO (Mock), rapamycin (5μm/ml), or infected with influenza for 24 hours. In mock cells, no evident autophagic fluorescent puncta were observed. Following rapamycin treatment, the co-localization of GFP and RFP resulted in increased yellow fluorescence, indicative of induced autophagy. Autophagy was efficiently initiated and could form a complete autophagic flux. However, upon infection with influenza, the GFP-RFP-LC3B fusion protein prominently localized to green fluorescence, with minimal red fluorescent puncta, suggested influenza ability to activate autophagy while hindering the formation of a complete autophagic flux. Representative immunofluorescence images are shown. \u003cstrong\u003e(D) \u003c/strong\u003eThe experimental procedure for co-infection of bacteria and influenza virus.\u003cstrong\u003e (E) \u003c/strong\u003eWestern blot analysis was utilized to evaluate the lipidation status of LC3B (LC3B II) and the accumulation of SQSTM1/p62 protein, as well as influenza virus NP protein and M1 protein, following bacterial-viral coinfection. \u003cstrong\u003e(F) \u003c/strong\u003eCellular toxicity was assessed after 24 hours of influenza infection alone or bacterial-viral coinfection.\u003cstrong\u003e (G) \u003c/strong\u003eFluorescence micrographs depict A549 cells expressing GFP-RFP-LC3B, subjected to infection with influenza alone or bacterial-viral coinfection for 24 hours.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4612057/v1/eb2adcc9e75be24a10caae08.jpg"},{"id":75351261,"identity":"67db4853-0e73-4b13-9697-69c0fe149052","added_by":"auto","created_at":"2025-02-03 16:08:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":18670242,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4612057/v1/550841e0-e32a-485e-8685-0697b7ca548f.pdf"},{"id":60415211,"identity":"103eee01-1f1d-49bb-9893-1760d90cb68c","added_by":"auto","created_at":"2024-07-16 13:46:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2831627,"visible":true,"origin":"","legend":"","description":"","filename":"Microbiomesupplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-4612057/v1/7017c9f4e41114b4c6e12c13.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nasal microbiota homeostasis regulates host anti-influenza immunity via the IFN and autophagy pathways in beagles","fulltext":[{"header":"Background","content":"\u003cp\u003eRespiratory tract infections (RTIs) persist as a substantial global health challenge[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. 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[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This link may be elucidated through direct microbe-microbe interactions, pathogen exclusion, or intricate host-microbe cross-talk[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. 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[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The hindrance of this initial stage of pathogenesis in respiratory infections by the resident microbiota, referred to as 'colonization resistance,' could play a crucial role in maintaining respiratory health[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, numerous studies have demonstrated the intricate and complex interactions between microorganisms and hosts in different ecological niches, such as the brain-gut axis, lung-gut axis, and liver-gut axis. This further enhances our understanding of symbiotic microbes within the human body[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The oral and upper respiratory tracts are closely linked anatomically and physiologically with the lower respiratory tract including the lung, and the influence of oral and upper respiratory microbes on the lung microbiota is increasingly being recognized[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Previous studies have reported correlations between microbiome composition and various physiological characteristics and disease manifestations in the nasal cavity and lung[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Notably, unlike the oral cavity and gut, which harbor a substantial bacterial load teeming with millions of viable and replicating microbes, the lower airways have been considered a sterile environment because of the necessity that a thin bronchial epithelial fluid lining is maintained to enable efficient gas exchange[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, with the advancement in microbiome research, it is increasingly acknowledged that the pulmonary environment hosts a diverse microbial community[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. 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[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe 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[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The dysbiosis of the airway microbiota has been linked to the acceleration of lung function decline in chronic obstructive pulmonary disease[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The transient colonization of the lungs by oral and airway microbiota can modulate lung immune responses and inflammation thresholds, reducing susceptibility to \u003cem\u003eStreptococcus\u003c/em\u003e pneumoniae and expediting its clearance[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Several studies have indicated an association between coronavirus disease (COVID-19)[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], respiratory syncytial virus (RSV)[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] or influenza A virus (IAV)[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] infection and the upper respiratory tract 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[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, the direct association between nasal microbiota and susceptibility to influenza viruses, along with its underlying mechanisms, remains unclear.\u003c/p\u003e \u003cp\u003eIn this study, we established a model of nasal microbiota dysbiosis in three-month-old beagles by locally applying a combination of mupirocin and neomycin ointment to the nasal cavity, and revealed that nasal microbiota dysbiosis diminishes the interferon (IFN)-mediated antiviral response, exacerbates influenza-induced mucosal barrier disruption, and heightens host susceptibility to influenza virus. Interestingly, we observed that nasal microbiota dysbiosis exacerbates lung microbiota dysbiosis after influenza virus infection, lowers the inflammation threshold in the lung, and worsens clinical manifestations and pulmonary pathology. Furthermore, we have identified two commensal bacterial genus, 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 virus infection. Further study reveals a key role of \u003cem\u003eLactobacillus plantarum\u003c/em\u003e C123(\u003cem\u003eL.p\u003c/em\u003e) in modulating the antiviral response. As far as we know, such correlation has not been reported to date.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eVirus, bacteria and cell strains\u003c/h2\u003e \u003cp\u003eThe viral strain of the H3N2 subtype, A/Canine/Jiangsu/06/2010 (JS/10), was used for 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) cells and titered by plaque assay as previously reported[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The detailed information on lactic acid bacteria in this study is shown in Supplementary Table\u0026nbsp;1. A549 cells (ATCC CCL-185) were cultured in F-12K complete medium (90% F-12K\u0026thinsp;+\u0026thinsp;10% FBS), while canine kidney cells (MDCK, ATCC CCL-34) and 293T cells (ATCC CRL-3216) were cultured in DMEM complete medium (90% DMEM\u0026thinsp;+\u0026thinsp;10% FBS)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAnimals and grouping\u003c/h2\u003e \u003cp\u003eThree-month-old conventional beagle puppies ( Anlimao Biotechnology, Yizheng, China) in this study 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[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. With a subsequent routine examination at the veterinary teaching hospital of Nanjing Agricultural University, these animals were demonstrated to be negative for canine coronavirus, canine parvovirus, canine distemper virus, and canine parainfluenza virus. Experimental dogs were collectively housed at the experimental animal center of Nanjing Agricultural University for one week. Subsequently, they were randomly divided into three groups (Nor, WT and Abx) and placed in separate rooms. Each group comprises 9 dogs, consisting of 4 females and 5 males. The Nor group functions as the experimental negative control, receiving no interventions (neither antibiotic treatment nor virus inoculation). Furthermore, during the respective stages (antibiotic treatment and virus inoculation phases), PBS is employed as a substitute for antibiotics and virus treatment. The WT group, during the antibiotic treatment phase, substitutes PBS for antibiotics. In the virus infection stage, intranasal inoculation was performed with 1 mL of JS/10 at a concentration of 10\u003csup\u003e7\u003c/sup\u003e PFU/ mL. The Abx group received continuous nasal application of mupirocin and neomycin ointment (10 mg per nostril, administered twice a day) for three days. On the challenge day (day 0), nasal samples were collected from the dogs, followed by intranasal inoculation with 1 mL of JS/10 at a concentration of 10\u003csup\u003e7\u003c/sup\u003e PFU/mL for the Abx and WT groups. 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).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e16S rRNA gene sequencing\u003c/h2\u003e \u003cp\u003eFor the nasal microbiota collection at three stages (pre-antibiotic treatment, post-antibiotic treatment, and post-virus infection), samples were acquired from three-month-old Beagles using sterile gloves (Qiagen). Following sampling, swabs were promptly returned to the collection tube and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until retrieval for subsequent analysis. Concerning the extraction of genomic DNA from the nasal microbiota, nasal swabs were vortexed for 2 minutes in 1 mL of modified liquid Amies transport medium. Subsequently, 500 \u0026micro;L samples from each swab were centrifuged at 13,000 \u0026times; \u003cem\u003eg\u003c/em\u003e at 4\u0026deg;C for 10 minutes. The resulting pellets were then dissolved in Buffer ALT with lysozyme (Sigma) and lysostaphin (Sigma) and incubated for 30 minutes at 37\u0026deg;C, followed by DNA extraction in accordance with 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) sequencing platform. For the lung microbiota collection, lung tissue sampling was performed using the biopsy punch plunger (Φ 4.mm, Miltex) according to a pre-designated experimental protocol (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The sampled tissues were promptly placed into sterile 1.5 mL EP tubes and stored at -80\u0026deg;C until retrieval for 16S rRNA gene sequencing. For the extraction of genomic DNA from the lung microbiota, lung tissues were homogenized for 10 minutes in 1 mL of modified liquid Amies transport medium. Subsequently, 500 \u0026micro;L aliquots from each homogenate were centrifuged at 1,000 \u0026times; \u003cem\u003eg\u003c/em\u003e at 4\u0026deg;C for 10 minutes to remove large tissue fragments. Following this, another set of 500 \u0026micro;L aliquots from each homogenate was centrifuged at 13,000 \u0026times; \u003cem\u003eg\u003c/em\u003e at 4\u0026deg;C for 10 minutes. The DNA extraction process and 16S rRNA gene sequencing followed the same steps as in nasal microbiota extraction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eNasal bacterial load analysis\u003c/h2\u003e \u003cp\u003eBacterial loads were quantified in triplicate by SYBR green qPCR as previously described[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Briefly, Quantitative PCR (qPCR) was performed using the StepOnePlus (Applied Biosystems) and the AceQ qPCR SYBR Green Master Mix (High ROX Premixed, Vazyme). A 253 bp product was generated using the primers: 520F, 5\u0026rsquo;- AYTGGGYDTAAAGNG and 820R, 5\u0026rsquo;-TACNVGGGTATCTAATCC. All reactions were performed in triplicate and included standards and non-template controls. Standards were generated from near full length cloned 16S rRNA gene of \u003cem\u003eEscherichia coli\u003c/em\u003e DH5α. Plasmids quantified using Quantit picogreen dsDNA Assay kit (Promega, Madison, USA), samples were then serially diluted 10-fold to form standards ranging from 1\u0026times;10\u003csup\u003e8\u003c/sup\u003e -1\u0026times;10\u003csup\u003e4\u003c/sup\u003e. Reactions consisted of 10 \u0026micro;L of SYBR Fast qPCR Master mix, 0.4 \u0026micro;L of 10\u0026micro;M dilutions primers and 7.2 \u0026micro;L of nuclease free PCR water (Biosharp). 2 \u0026micro;L of template DNA was added to each reaction. Cycling conditions were: 90\u0026deg;C for 5 minutes followed by 40 cycles of: 95\u0026deg;C for 30 seconds, 50\u0026deg;C for 15 seconds, and 72\u0026deg;C for 15 seconds. Melt curves were run as default from 60\u0026deg;C to 95\u0026deg;C, over 15 minutes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMicrobiota sequence data analysis\u003c/h2\u003e \u003cp\u003eThe downstream amplicon bioinformatic analyses were performed with EasyAmplicon pipeline (v1.15)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The nonredundant sequences were denoised into ASVs via the -unoise3 command of USEARCH (v10.0)[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The feature table was created with VSEARCH (v2.15)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. 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) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. 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) was utilized to identify significantly differential features between groups, and the Benjamini-Hochberg or Tukey-Kramer method was used to control the FDR [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The R package igraph (v1.5.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/igraph\u003c/span\u003e\u003cspan address=\"https://github.com/igraph\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized for differential ASVs correlation analysis, followed by visualization using Cytoscape (v3.8.2). The R package ggcor (v0.9.8.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/Github-Yilei/ggcor\u003c/span\u003e\u003cspan address=\"https://github.com/Github-Yilei/ggcor\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) 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[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMetagenomic pathway prediction\u003c/h2\u003e \u003cp\u003ePICRUSt2 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. R package ggplot2(v3.4.3) was utilized for visualization of the identified pathways. The Pearson correlation between statin-associated pathways and important statin-associated ASVs was calculated with R package igraph (v1.5.1) and visualized using the ggplot2(v3.4.3) and pheatmap (v1.0.12).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eHistological examination\u003c/h2\u003e \u003cp\u003eTissues were fixed in 4% paraformaldehyde at 4\u0026deg;C for 3 days and then embedded in paraffin, and were sectioned at 4 \u0026micro;m. The sections were deparaffinized in xylol and rehydrated in a graded series of ethanol and water, and then stained with haematoxylin and eosin. The tracheal and nasal mucosae were measured at thirty random unilateral points using SlideViewer imaging software and PANNORAMIC\u0026reg; 250 Flash III DX (3DHISTECH Ltd), and the mean values of the thickness were calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eImmunofluorescence analysis\u003c/h2\u003e \u003cp\u003eImmunofluorescence 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) for 20 min. Then samples were blocked with the blocking buffer (Beyotime) for 10 min. The sections were sequentially incubated with the primary antibody at 4\u0026deg;C overnight and anti-rabbit secondary antibody conjugated with Alexa Fluor 488 (Ptoteintech) or anti-mouse secondary antibody conjugated with Alexa Fluor 555 (Ptoteintech) at room temperature for 1 h. In this study, the following primary antibodies were used at 1:100 dilution: influenza virus NP mouse polyclonal antibody (laboratory preparation), TJP1/ZO-1 rabbit polyclonal antibody (Bioss), MAP1LC3B/LC3B rabbit monoclonal antibody (Abmart) or SQSTM1/p62 mouse monoclonal antibody (Abmart). After washing in PBS, nuclei were counterstained with DAPI (4',6-diamidino-2-phenylindole) and coverslips were mounted with Antifade Mounting Medium (Beyotime). Images were acquired using a Zeiss LSM 970 microscope, following morphometric analysis employing ZEISS ZEN (v3.9) and ImageJ[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eWestern blot analysis\u003c/h2\u003e \u003cp\u003eThe procedure of Western blot was performed as described earlier. Briefly, after protein samples were extracted from tissues, the concentration of protein was determined spectrophotometrically at 562 nm using the Protein Quantitative Reagent Kit-BCA Method (Epizyme Biomedical Technology). All samples were diluted to the same concentration and added to 5\u0026times; SDS-PAGE sample loading buffer (Beyotime). Thermic denaturation was promoted at 99\u0026deg;C for 5 min. For western blot assays, proteins were separated on an electrophoretic run and transferred on a 0.22 \u0026micro;m PVDF membrane (Merck Millipore). The membranes were blocked in 5% (w/v) non-fat milk in TBST (TBS\u0026thinsp;+\u0026thinsp;0.1% (v/v) Tween-20) and incubated overnight with the primary antibodies at 4\u0026deg;C. The following primary antibodies were used at 1:1000 dilution: anti-M1, anti-NP (laboratory preparation), phospho-TBK1 (Ser172) (Abmart), TBK1 (Abmart), phospho-IRF3 (S386) (Abmart), IRF3 (Abmart), phospho-NF-κB/p65 (Ser536) (Abmart) and NF-κB/p65 (Ser536) (Abmart) antibodies, MAP1LC3B/LC3B (Abmart), SQSTM1/p62 (Abmart), GAPDH antibody (Abmart) was used as loading controls. After washing in TBST, the horseradish peroxidase (HRP)-conjugated anti-rabbit or anti-mouse secondary antibody (Ptoteintech) were used at 1:5000 dilution for 90 min at room temperature. Relative protein expression was quantified by ECL (Epizyme Biomedical Technology) and visualized on the ChemiDoc Imaging system (Bio-Rad). Band densitometry was performed on the ImageJ software and normalized for the control group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eQuantification of mRNA expression\u003c/h2\u003e \u003cp\u003eTotal RNA was isolated from the tissue, whole blood with Total RNA Kit (Omega Bio-tek), according to the manufacturer\u0026rsquo;s instructions. After purity and quality checks, mRNA was converted into complementary DNA (cDNA) with a High-Capacity cDNA Reverse Transcriptase kit (Vazyme). The cDNA was diluted 1:10 (v/v) by RNase-free water (Biosharp). Relative mRNA expression was quantified by qPCR analyses on a StepOnePlus (Applied Biosystems), using AceQ qPCR SYBR Green Master Mix (High ROX Premixed, Vazyme). 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\u0026nbsp;2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRNA sequencing (RNA-seq) analysis\u003c/h2\u003e \u003cp\u003eUtilizing the Trizol method, total RNA from eukaryotic organisms was extracted. After purity assessment via NanoDrop One, the integrity of the extracted RNA was evaluated employing the Agilent 2100 systems. The mRNA from the total RNA pool was enriched and purified with the VAHTS mRNA Capture Beads reagent kit (Vazyme), and then was fragmented via ion shearing to achieve fragments within the range of 250\u0026ndash;450 base pairs, 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\u0026ndash;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 12K (Applied Biosystems) fluorescence quantification assays. Ultimately, second-generation sequencing was conducted on the Illumina NovaSeq 6000 platform (Illumina), 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). Alignment to the reference genome (Dog10K_Boxer_Tasha, GCF_000002285.5) was executed through STAR (v2.7.10). 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). Enrichment analyses for KEGG and GSEA pathways were conducted via clusterProfiler (v4.8.2)[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Differential gene clustering based on k-means clustering occurred through the STRING online platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and ClusterGVis(v0.0.2)[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. All plots were generated using ggplot2 (v3.4.3) and pheatmap (v1.0.12).\u003c/p\u003e \u003cp\u003e \u003cb\u003eCo-infection assay in\u003c/b\u003e \u003cb\u003evitro\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA549 cells were cultured in F12K medium (Gibco) supplemented with 10% fetal bovine serum (Gibco). Seeded at a density of 1.0\u0026times;10\u003csup\u003e6\u003c/sup\u003e cells/ml in 2 mL in 6-well plates, cells were incubated at 37\u0026deg;C with 5% CO\u003csub\u003e2\u003c/sub\u003e until attaining 80% confluency. Following PBS washes, cells were infected with JS/10 at an MOI\u0026thinsp;=\u0026thinsp;1 and incubated for 1 hour at 37\u0026deg;C with 5% CO\u003csub\u003e2\u003c/sub\u003e, then washed thrice with PBS and replenished with 2 mL of virus infection maintenance medium (0.125 \u0026micro;g/mL TPCK-trypsin in Opti-MEM). Lactic acid bacteria were statically incubated at 37\u0026deg;C in MRS broth (Sigma-Aldrich) until reaching an OD\u003csub\u003e600\u003c/sub\u003e of 0.5. Subsequent PBS washes were followed by resuspension in virus infection maintenance medium, adjusting the OD\u003csub\u003e600\u003c/sub\u003e to 1. The bacterial suspension, at an MOI of 10, was subsequently added to A549 cells previously infected with JS/10. At specific time points, cell lysates were collected using RIPA buffer (containing 1% protease inhibitor and phosphatase inhibitor), and the total protein concentration was determined using the BCA assay. Lysates were stored at -80\u0026deg;C until further analysis by SDS-PAGE and western blotting.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLuciferase reporter assays\u003c/h2\u003e \u003cp\u003eA549 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 hours post-transfection, the cells were washed with PBS and then infected with JS/10 at an MOI of 1. After a 1-hour incubation at 37\u0026deg;C with 5% CO\u003csub\u003e2\u003c/sub\u003e, the cells were washed three times with PBS and replenished with 0.5 mL of virus infection maintenance medium (0.125 \u0026micro;g/mL TPCK-trypsin in Opti-MEM). Lactic acid bacteria were statically incubated at 37\u0026deg;C in MRS broth (Sigma-Aldrich) until reaching an OD\u003csub\u003e600\u003c/sub\u003e of 0.5. The cultures were then washed with PBS and resuspended in virus infection maintenance medium, adjusting the OD\u003csub\u003e600\u003c/sub\u003e to 1. This bacterial suspension was added to A549 cells that had been previously infected with JS/10. At 24 hours post-infection, the cells were lysed with lysis buffer, and luciferase activities were measured using a dual luciferase assay kit (Promega, Madison, WI, USA) according to the manufacturer's instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCytotoxicity assay\u003c/h2\u003e \u003cp\u003eCell viability was detected using CCK-8 assay (Beyotime). In brief, A549 cells were cultured in F12K medium (Gibco) supplemented with 10% fetal bovine serum (Gibco). Seeded at a density of 0.5 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells/mL in 100 \u0026micro;L in 96-well plates, cells were incubated at 37\u0026deg;C with 5% CO\u003csub\u003e2\u003c/sub\u003e until attaining 80% confluency. Then it was infected with 100 \u0026micro;L of bacterial suspension at an MOI of 1:10 and virus suspension at an MOI of 1:1 and incubated for 12 hours or 24 hours at 37\u0026deg;C with 5% CO\u003csub\u003e2\u003c/sub\u003e. At the indicated time points, 10 \u0026micro;L CCK-8 solution was added into each well and incubated at 37\u0026deg;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 or cells infected with influenza virus alone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLentiviral transduction\u003c/h2\u003e \u003cp\u003e293T cells were cultured in DMEM medium (Gibco) supplemented with 10% fetal bovine serum (Gibco) and seeded at a density of 1.0\u0026times;10\u003csup\u003e6\u003c/sup\u003e cells/mL in 2 mL in 6-well plates. Subsequently, the plasmids plvx-GFP-RFP-hLC3B, pMD2.G, and pSPAX2 were co-transfected into the 293T cells using Lipofectamine 2000 (Thermo Fisher Scientific). Cell culture supernatants were collected at 48 hours and 72 hours post-transfection, filtered through a 0.45 \u0026micro;m filter, and used to infect A549 cells for 24 hours. Following infection, selection pressure was applied using 2 \u0026micro;g/mL puromycin (MCE), and single-cell clones were obtained using limiting dilution method. The cells were cultured in complete medium (F12K, 10% fetal bovine serum) supplemented with 5 \u0026micro;M/mL rapamycin (MCE) for 12 hours. Autophagosome formation was then observed under a fluorescence microscope.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDysbiosis of the nasal microbiome exacerbates influenza-induced respiratory disease\u003c/h2\u003e \u003cp\u003e To elucidate the potential connection of nasal microbiota homeostasis to influenza susceptibility, we created a model of nasal microbiota dysbiosis in three-month-old beagles by locally applying a combination of mupirocin and neomycin ointment to the nasal cavity. Nasal swabs were collected before and after the antibiotic treatment, and 16S rRNA sequencing was employed to assess the changes in the nasal microbiome. The absolute abundance of nasal microbiota was evaluated via quantitative real-time PCR (RT-qPCR). As anticipated, short-term administration of combination antibiotics in the nasal cavity significantly reduced the microbial absolute abundance (Fig.\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). Considering the significant decrease in bacterial abundance following treatment with compounded antibiotics, subsequent analyses were conducted based on the relative abundance obtained through equal-weight resampling. We observed a relatively decreased α-diversity after antibiotic treatment in both Chao1 and Richness indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Furthermore, principal coordinate analysis (PCoA) based on Bray-Curtis distances indicated significantly different nasal microbial structures compared with those before antibiotic treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Then we conducted differential analyses at the phylum and genus levels. We noticed that combination antibiotic treatment caused expansion of the phylum Proteobacteria (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB), which is considered as a signature of gut dysbiosis[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. We also observed a significant increase in the ratio of \u003cem\u003ePsychrobacter\u003c/em\u003e, \u003cem\u003eAchromobacter\u003c/em\u003e, \u003cem\u003eRalstonia\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e and \u003cem\u003eEscherichia-Shigella\u003c/em\u003e, along with a decrease in the abundance of \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eLeucobacter\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eLachnoclostridium\u003c/em\u003e at the genus level (Fig.\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Benjamini-Hochberg).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo gain further insights into the potential impact of antibiotic-mediated nasal microbiome dysbiosis on host functionality, the PICRUSt2, a robust tool for predicting functional pathways base on microbial community composition[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], was utilized to assess the influences of antibiotic treatment on the contributions of microbiomes to host-associated pathways. Subsequently, the EasyAmplicon package was used for KEGG (Kyoto Encyclopedia of Genes and Genomes, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kegg.jp/\u003c/span\u003e\u003cspan address=\"https://www.kegg.jp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) three-level classification and the DESeq2 package was used for differential analysis at the KEGG third level[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. We observed significant differences in the viral infection and apoptosis pathways affected by the microbiomes between before and after antibiotic treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Given this, we hypothesize that the nasal microbiome disturbance caused by short-term antibiotic treatment might affect the host's ability to resist influenza infection. To explore this, we administered a combination of antibiotics to beagles and then inoculated in nasal with 10\u003csup\u003e7\u003c/sup\u003e PFU (plaque forming unit) of H3N2 virus (A/canine/Jiangsu/06/2011, JS/10) (Abx group). The dogs without antibiotic treatment but with virus infection were categorized as the infection control group (WT group), and the untreated and uninfected dogs served as the normal control group (Nor group). The experiment was divided into three stages: the Before stage, which indicates the period prior to antibiotic treatment; the Clean stage, representing the period following antibiotic treatment but before viral infection; and the Infected stage, denoting the period after viral infection. A detailed overview of the experimental workflow was provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003ed. 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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eE) and weight loss (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). 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 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The data indicated significant differences in clinical score among the three groups, with the Abx group presenting the highest clinical score (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eG, p\u0026thinsp;\u0026lt;\u0026thinsp;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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eH, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Tukey's HSD).\u003c/p\u003e \u003cp\u003eOn the eighth day of infection, extensive hemorrhagic spots were observed 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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eI). 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, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eI, Fig. S2B). Then we scored the histopathological changes in the nasal, tracheal, and lung tissues based on the evaluation criteria outlined and described elsewhere[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eJ, p\u0026thinsp;\u0026lt;\u0026thinsp;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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eK, L, p\u0026thinsp;\u0026lt;\u0026thinsp;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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCommunity dynamics and functional changes of nasal microbiota during respiratory tract infection\u003c/h2\u003e \u003cp\u003eMicrobiota residing in the nasal cavity have been reported to be associated with susceptibility to and severity of RTIs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. To explore which microbes play a pivotal role in the host's resistance to influenza infection, we characterized the bacterial compositions to reveal differences in the microbial communities among the Nor, WT and Abx groups. The ternary plot indicates that regardless of influenza virus infection, the high-abundance microbial communities (genus level: relative abundance\u0026thinsp;\u0026gt;\u0026thinsp;0.5%) in the Abx group showed a significant loss after antibiotic treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, Fig. S5A). Meanwhile, during the entire experimental period, the Chao1 and Richness diversity indices in the Abx group showed a sustained decline, a trend that continued even post-viral infection. (Fig. S4A, B; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). To evaluate the similarity of the bacterial communities among the above three groups, the PCoA was performed using the Bray-Curtis distance matrix. 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, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Wilcoxon rank-sum test). However, regardless of virus infection, no significant differences were observed between the Nor and WT groups (Fig. S4C, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Wilcoxon rank-sum test).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further explore the bacterial genus enriched in the Nor, WT and Abx groups at different stages (Before, Clean, Infected), a differential enrichment analysis was conducted using DESeq2, combined with one-way ANOVA. Relative abundance analysis revealed that compared to the WT group, a diminished proportion of bacterial genus \u003cem\u003eLactobacillus\u003c/em\u003e was identified in the Abx group, while an inverse trend was observed for \u003cem\u003eMoraxella\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, Fig. S5B, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Tukey's HSD). To further ascertain whether this change was attributable to antibiotic treatment or influenza infection, we conducted intra-group comparisons for Abx and WT groups before and after virus infection. Interestingly, we found that, irrespective of the Abx or WT group, the relative abundance of \u003cem\u003eLactobacillus\u003c/em\u003e showed no significant difference before and after virus infection. In contrast, for \u003cem\u003eMoraxella\u003c/em\u003e, 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 (Fig. S6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Tukey's HSD). Then we conducted a correlation analysis on the microbial communities before and after infection in both the Abx and WT groups. After antibiotic treatment, the proportions of \u003cem\u003eLactobacillus\u003c/em\u003e were observed to be negatively associated with \u003cem\u003eMoraxella\u003c/em\u003e. Additionally, after virus infection, the proportions of \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eMegamonas\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e_9 and \u003cem\u003eLachnoclostridium\u003c/em\u003e were observed to have a negative association with \u003cem\u003eMoraxella\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, Fig. S7, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). The virus titers presented negative correlations to the bacterial genus \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eMegamonas\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e_9 and \u003cem\u003eLachnoclostridium\u003c/em\u003e, but a positive association with \u003cem\u003eMoraxella\u003c/em\u003e. Similarly, such changes of \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eMoraxella\u003c/em\u003e were also observed in dogs with antibiotic treatment but not infected with influenza virus (Fig. S8).\u003c/p\u003e \u003cp\u003eFunctional analysis of nasal microbiota based on the PICRUSt2 and KEGG database revealed substantial differences among the Nor, WT and Abx groups after antibiotic treatment. Notably, these differences encompass pathways associated with the infection of pathogenic microorganisms, including Kaposi sarcoma-associated herpesvirus (KSHV) infection, Herpes simplex virus 1 (HSV-1) infection, Hepatitis C virus (Hepacivirus C), Human cytomegalovirus (HCMV) infection, Human immunodeficiency virus 1 (HIV-1) infection, Epstein-Barr virus (EBV) infection, Hepatitis B and Influenza A, cell junctions (tight, adherens, and gap junctions), as well as autophagy and 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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eE,). The data led us to speculate that these differences in functional pathways might correlate with changes in \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eMoraxella\u003c/em\u003e. Therefore, we conducted a correlation analysis between the abundance of the two bacterial genus before and after infection and their contribution to pathways. Our data indicate that after antibiotic treatment, \u003cem\u003eLactobacillus\u003c/em\u003e had a significant negative correlation with the pathway associated with pathogenic infections; in contrast, \u003cem\u003eMoraxella\u003c/em\u003e exhibited a significant positive correlation with the pathway associated with pathogenic infections (Fig. S10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Benjamini-Hochberg). Given these data, we speculate that the abundance of \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eMoraxella\u003c/em\u003e in the nasal microbiome may be associated with susceptibility to influenza infection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eDysbiosis of the nasal microbiome diminishes the antiviral response within the nasal cavity\u003c/h2\u003e \u003cp\u003eTo deeper 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. 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 up-regulated and 308 genes down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). A total of 2784 DEGs yielded by inter-group comparisons (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) were subjected to clustering analysis based on the Fuzzy C-means (FCM) algorithm, resulting in the identification of 5 distinct clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Notably, Cluster3 and Cluster5 displayed contrasting trends. Specifically, Cluster3 predominantly comprised inflammation-related genes (e.g., NLRP3, IL1β), while Cluster5 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\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Benjamini-Hochberg). Further, the gene set enrichment analysis (GSEA) for the Abx group identified a significant enrichment of the genes associated with viral disease, including influenza A, coronavirus disease (COVID-19), Epstein-Barr virus (EBV) infection, Hepatitis C, herpes simplex virus 1 (HSV-1) infection, human immunodeficiency virus 1 (HIV-1), Hepatitis B, and human T-cell leukemia virus 1 (HTLV-1) infection. Additionally, some inflammation-related pathways were enriched, including IL-17, tumor necrosis factor (TNF), RIG-I-like receptor, Toll-like receptor, cytosolic DNA-sensing, NOD-like receptor, and Janus kinase (JAK)-signal transducer and activator of transcription (STAT) signaling pathways (Fig.S11B, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Benjamini-Hochberg). The protein-protein interaction (PPI) networks for DEGs were subsequently constructed using STRING (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with a minimum required interaction score of 0.4. We can effectively categorize the DEGs into five distinct clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). 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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). In Cluster 1, notably, genes involved in regulating interferon production (IFIH1, IRF1, IRF9, STAT1 and STAT2) and interferon-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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Benjamini-Hochberg). Therefore, we speculate that the disruption of nasal microbiota may, to some extent, attenuate the host's antiviral immune response. Additionally, the transcript level of cytokines and pertinent signaling pathways in the bloodstream following viral infection (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eF) showed a consistent trend with that observed in the nasal transcriptome analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOur 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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, Cluster 2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;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[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. 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 virus 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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, Cluster 3,). Therefore, we further conducted an immunofluorescence analysis of the tight junction protein ZO-1 (TJP1), and demonstrated that ZO-1 expression at the infection site was lower in the Abx group than in the WT group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDisruption of nasal microbiota exacerbates the dysbiosis of lung microbiota following influenza infection.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWhile the upper airway accommodates the most substantial biomass and stable microbial communities, the lungs are continually exposed to these bacteria through micro-aspiration. Given this, we pose a question: Can nasal microbiota disruption lead to changes in lung microbiota, and thus exacerbate 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. To investigate the composition and distribution characteristics of lung microbiota among different groups, we utilized the EasyAmplicon package for taxonomy analysis. Our results reveal that at the phylum level, \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eBacteroidetes\u003c/em\u003e, \u003cem\u003eActinobacteria\u003c/em\u003e, \u003cem\u003eFusobacteria\u003c/em\u003e and \u003cem\u003eAcidobacteria\u003c/em\u003e dominate the bacterial taxa in the canine lung (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). At the genus level, \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eMoraxella\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e and \u003cem\u003eNitratireductor\u003c/em\u003e constitute the predominant microbial communities in the canine lung (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). This composition bears resemblance to the microbiota found in the human lung[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, the Abx group exhibited significantly lower relative abundances of Firmicutes and Bacteroidetes compared to the Nor and WT groups (Fig. S12A, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Tukey-Kramer). However, no significant differences were noted in Proteobacteria, Firmicutes and Bacteroidetes between the Nor and WT groups (Fig. S12A, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Tukey-Kramer). The differential analysis at the genus level revealed that, compared to the WT group, the relative abundances of \u003cem\u003eMoraxella\u003c/em\u003e, \u003cem\u003eNitratireductor\u003c/em\u003e, \u003cem\u003eMesorhizobium\u003c/em\u003e, \u003cem\u003eMarvinbryantia\u003c/em\u003e and \u003cem\u003eMycobacterium\u003c/em\u003e were significantly higher in the Abx group, but the opposite was true for \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eOdoribacter\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, Fig. S12B, C, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Tukey-Kramer). 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 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Tukey-Kramer), but no significant difference was found between the Nor and WT groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003eE, F). Further, we determine the contribution of pulmonary microbiota to host pathways. The functional analysis of pulmonary microbiota by PICRUSt2 indicates significant differences in several pathways between the Abx group and the WT group. These include the viral infection-related pathway (influenza A, Hepatitis B, Hepatitis C, HCMV infection, EBV infection, KSHV infection, HSV1 infection and HIV infection), autophagy-related pathway (mTOR signaling pathway), apoptosis pathway, cell junctions (tight junction, adherens junction and gap junction), and Toll and Imd signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003eG, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Benjamini-Hochberg). Additionally, the two bacterial genera, \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eVeillonella\u003c/em\u003e, 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, \u003cem\u003eMycobacterium\u003c/em\u003e, \u003cem\u003eMesorhizobium\u003c/em\u003e and \u003cem\u003eNitratireductor\u003c/em\u003e exhibited a distinct positive correlation with viral infection-related pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003eH, S13, p\u0026thinsp;\u0026lt;\u0026thinsp;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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eDisruption of lung microbiota exacerbates inflammatory response and barrier damage in influenza infection\u003c/h2\u003e \u003cp\u003eTo better comprehend the potential impact of lung microbiota dysbiosis following influenza infection, we conducted transcriptome sequencing on three groups of lung tissues and performed differential analysis on mRNA expression matrices using the DEseq2 package. 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 up-regulation while 305 genes exhibited down-regulation in the Abx group (Fig.S14A). The union of DEGs from inter-group comparisons yielded a total of 2,225 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 differentially expressed genes in the lung transcriptome. Cluster 2 includes several canonical interferon-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 differentially expressed genes 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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). 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 Cluster 1, 2 and 3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Normalization of the expression levels of these genes based on the Nor group showed that interferon-stimulated genes (ISGs), including OAS1, OAS2, Mx1, Mx2, ISG15, IFIT2, IFIT3 and TRIM25, were significantly downregulated in the Abx group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, ISGs, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;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.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Benjamini-Hochberg). 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[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], were consistently upregulated in the Abx group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, p\u0026thinsp;\u0026lt;\u0026thinsp;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.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, p\u0026thinsp;\u0026lt;\u0026thinsp;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.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo 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 indicates a significant positive correlation between antivirus-related (IFNβ1, IFNα, OAS1, Mx1, PKR, ISG15, Myd88, Mx2) and inflammation-related (IL6, TNFα, Bax, Caspase3) genes and \u003cem\u003eLactobacillus\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Mantel's \u003cem\u003eR\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.4), but a notable negative correlation between the above genes and Moraxella (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Mantel's \u003cem\u003eR\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.4). Based on Pearson correlation coefficient analysis, \u003cem\u003eLactobacillus\u003c/em\u003e exhibited a significant positive correlation with the transcription of IFNβ1 and Myd88 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, Pearson's \u003cem\u003eR\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.7, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Benjamini-Hochberg), but a significant negative correlation with TNFα and caspase3 transcription (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, Pearson's \u003cem\u003eR\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;0.7, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Benjamini-Hochberg). In contrast, Moraxella demonstrated a completely opposite trend to \u003cem\u003eLactobacillus\u003c/em\u003e. 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 (TANK-binding kinase 1) were higher, while the phosphorylation levels of inflammation-related proteins, for example, NF-κB/p65, were lower in the Abx group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). This finding further confirms that microbial dysbiosis in the lung diminishes the host's antiviral response.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLactobacillus\u003c/b\u003e \u003cb\u003eexerts antiviral effects in\u003c/b\u003e \u003cb\u003evitro\u003c/b\u003e \u003cb\u003eby activating IFN-mediated pathways.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eLactic acid bacteria (LAB) are known as probiotic organisms and have been increasingly reported to exert powerful biological actions. In this study, we isolated ten strains of LAB from the nasal cavity, oral cavity and rectum of experimental beagles and conducted in \u003cem\u003evitro\u003c/em\u003e antiviral assays in two different setups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Our data showed that \u003cem\u003eLactobacillus plantarum\u003c/em\u003e C123 (\u003cem\u003eL.p\u003c/em\u003e) exhibited significant in \u003cem\u003evitro\u003c/em\u003e antiviral activity, whether by pre-treating A549 cells before influenza infection or co-infection with influenza virus (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Cytotoxicity evaluation using CCK-8 assay showed that \u003cem\u003eLactobacillus plantarum\u003c/em\u003e C123 did not exhibit cytotoxicity against A549 cells. Further, we used the dual-luciferase reporter assay to evaluate IFN-β and NF-κB/p65 promoter activities, and found that \u003cem\u003eLactobacillus plantarum\u003c/em\u003e C123 could significantly enhance the activation of the IFN-β promoter, but not affect the NF-κB/p65 promoter activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). 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 interferons and is subject to tight regulation[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].To investigate whether the antiviral effects of \u003cem\u003eLactobacillus plantarum\u003c/em\u003e C123 rely on upstream regulation of the IFN pathway, we assessed TBK1 phosphorylation levels in A549 cells following exposure to \u003cem\u003eLactobacillus plantarum\u003c/em\u003e C123 and influenza infection. Interestingly, both total and phosphorylated TBK1 levels increased in the early stages of infection (1h, 4h, 8h) in both the pre-treatment and co-infection groups. However, at later stages (12h, 24h), both total and phosphorylated TBK1 levels significantly decreased in the co-infection group. Additionally, influenza virus NP protein levels were consistently lower in the co-infection group compared to the virus-infected group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003ed). Thus, we speculate that \u003cem\u003eLactobacillus plantarum\u003c/em\u003e C123 may activate additional antiviral pathways beyond the IFN axis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eLactobacillus\u003c/b\u003e \u003cb\u003einhibits virus replication by interfering with influenza-induced incomplete autophagy\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIt has been known that influenza viruses employ various strategies to enhance self-replication, including the initiation of autophagy process and its subsequent block of the fusion of autophagosomes with lysosomes[\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. TBK1 is a versatile serine/threonine protein kinase with established roles in innate immunity, metabolism, autophagy, cell death, and inflammation. TBK1 within cells can be degraded through the autophagy pathway[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In our investigation, we find the enrichment of autophagy-related pathways in both nasal and lung microbiota functional predictions, as well as in transcriptomic KEGG enrichment analyses. Moreover, our nasal transcriptome analysis reveals heightened expression levels of pivotal autophagy-related genes. (ATG9B, GABARAPL2, MAP1LC3B/LC3B and SQSTM1/p62) in the Abx group compared to the WT group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, Cluster5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Benjamini-Hochberg). This led to us to speculate that nasal microbiota might modulate host autophagy to suppress viral replication. Therefore, we further investigated the occurrence of autophagy at the nasal infection site in the three groups. Semi-quantitative fluorescence analysis revealed that, in the Nor group, the expression of MAP1LC3B/LC3B and SQSTM1/p62 in the nasal cavity was primarily localized in basal cells; in the WT group, expression of MAP1LC3B/LC3B and SQSTM1/p62 could be detected in both ciliated epithelial cells and basal cells; in the Abx group, expression of MAP1LC3B/LC3B and SQSTM1/p62 was elevated in basal cells, accompanied by a substantial accumulation of SQSTM1/p62 in tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). To investigate whether cellular autophagy levels are altered in response to influenza infection (strain used in this experiment), we stably transfected influenza-infected A549 cells with the GFP-LC3B. We observed a significant increase in GFP-LC3B autophagosomes in influenza-infected cells compared to uninfected cells at 24 hours post-infection (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). This elevated autophagosome count, along with blocked autophagic flux, was further confirmed by western blot analysis of endogenous lipidated and SQSTM1/p62 levels following influenza virus infection (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). To further investigate whether \u003cem\u003eLactobacillus\u003c/em\u003e can interfere with this process, we conducted in \u003cem\u003evitro\u003c/em\u003e co-infection experiments of \u003cem\u003eLactobacillus plantarum\u003c/em\u003e C123 and influenza virus, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003ed. The results revealed that compared to the single virus infection group, SQSTM1/p62 gradually accumulated with longer virus-exposure time, while in the co-infection group, SQSTM1/p62 levels exhibited a significant decrease at 24 hours post-infection, along with a marked reduction in intracellular NP and M1 proteins compared to the single virus infection group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003ee). To rule out the possibility that reduced viral titers were due to a decrease in cell viability caused by \u003cem\u003eLactobacillus\u003c/em\u003e, we assessed cytotoxicity at 24 hours post-infection. The results showed that \u003cem\u003eLactobacillus plantarum\u003c/em\u003e C123 mono-infection did not induce cytotoxicity, and no significant difference in cytotoxicity was found between the single virus infection and co-infection groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eF). We also stably transfected influenza-infected A549 cells with the GFP-RFP-LC3B, and observed a significant retention of green fluorescence in only influenza-infected cells compared to the co-infection cells at 24 hours post-infection (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eG). Based on these data, we speculate that nasal microbiota is involved in the regulation of autophagy and its flux during influenza infection, reversing the inhibition of host autophagic flux induced by influenza virus and accelerating the virus clearance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOver the years, a wealth of evidence suggests the role of bacterial communities in the respiratory tract in preventing respiratory pathogens from establishing an infection[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, most of what we know about the protection role of commensal bacteria stems from studies using mouse models, and relatively little information is available in dogs. The nasal cavity, an essential component of the upper respiratory tract, serves as the primary site for the initial contact of the influenza virus[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. One recent study indicates a possible relationship between the microbial composition of the nasal cavity and susceptibility to the influenza virus[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. But a clear association between nasal microbiota and susceptibility to influenza viruses, along with its underlying mechanisms, remains unclear.\u003c/p\u003e \u003cp\u003eTo investigate the potential role of nasal microbiota in the influenza virus infection, we created a model of nasal microbiota dysbiosis in three-month-old beagles by locally applying a combination of mupirocin and neomycin ointment to the nasal cavity[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Our data indicated that the dysregulated microbial profile in the nasal cavity displays significant variations in its contributions to functional pathways, particularly those associated with viral infection, inflammation and barrier functions, when compared to the normal microbial balance. Also, the antibiotic-treated dogs exhibited significantly exacerbated influenza infection in the respiratory tract compared with the vehicle-treated animals. Therefore, we posit that the disturbance in microbial composition of the nasal cavity may be one of the factors contributing to differential susceptibility of the host to viral infections. The analysis of microbial profiles associated with respiratory susceptibility to virus infection from two previous reports also yielded similar finding[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. This provides us with a hint that, maintaining the stability of the respiratory ecosystem is crucial for effectively controlling viral diseases.\u003c/p\u003e \u003cp\u003eIt is known that imbalances in microbial populations can directly lead to dysfunction in mucosal barrier [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], which is closely associated with inflammation[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Excessive inflammation triggered by innate immune cells may result in severe multi-organ pathologies, accompanied by an excessive expression of mucosal mucins, thereby interfering with antiviral immunity[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. In this study, we found substantial variations in microbial communities between the antibiotic-treated and the vehicle-treated groups after influenza infection. The relative abundance of \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e_9, \u003cem\u003eMegamonas\u003c/em\u003e and \u003cem\u003eLachnoclostridium\u003c/em\u003e in both the nasal cavity and lung is significantly reduced in the Abx group, and showed a near-perfect negative correlation with that of Moraxella. We speculate that changes in the relative abundance of these bacterial genera might contribute to influenza severity. Numerous studies suggest that the symbiotic bacteria with reduced abundance mentioned above can produce a variety of short-chain fatty acids (SCFAs) during the growth process[\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The SCFAs can stimulate the growth and/or activity of symbiotic bacteria and play a key role in maintaining health by regulating the intestinal barrier function and triggering local and systemic anti-inflammatory effects[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. A clinical study suggests that SCFAs may provide protection against severe influenza infection by reducing tissue damage and boosting adaptive anti-viral immunity[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Additionally, it has been reported that \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003ePrevotella\u003c/em\u003e have the capability to upregulate the expression of tight junction protein 1 (TJP1/ZO-1), thereby bolstering the integrity of the epithelial barrier[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Our study found, following viral infection, the Abx group displayed a significantly lower expression of TJP1 compared to the WT group. Also, massive colonization by \u003cem\u003eMoraxella\u003c/em\u003e has been demonstrated to induce a mixed proinflammatory immune response[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. \u003cem\u003eMoraxella\u003c/em\u003e can excessively activate the Toll-like receptor (TLR) signaling pathway, compromising the innate immune response of alveolar macrophages and contributing to exacerbations of chronic obstructive pulmonary disease (COPD) [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Our transcriptomic analysis of the lung 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 further inflammatory tissue damage [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Elevated expression of inflammatory cytokines in the Abx group has been revealed in our transcriptomic data. Additionally, previous studies have indicated that the excessive upregulation of TLR2 and TLR6 can, to a certain extent, promote the degradation of Neuropilin1 (NRP1) and Indian Hedgehog (IHH), thus reducing the expression of tight junction (TJ) proteins and ultimately weakening the epithelial barrier [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In agreement with this, our transcriptomic analysis demonstrated that TJ proteins, including TJP1, occludin and Cldn4, were upregulated in the Abx group. Also, we found that genes related to RhoA (Ras homolog gene family, member A) signaling pathway (Rac1, RHOA1, LIMK1 and LIMK2), linked to the destabilization of adherens junctions[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]were consistently upregulated in the Abx group. Although the relevant mechanisms underlying the influence of nasal microbiota on invading influenza virus is still unclear, these data clearly suggest that the microbiota play an important role in regulating host immunity and epithelial barrier function, thereby controlling the outcome of viral infection in the respiratory tract.\u003c/p\u003e \u003cp\u003eMucus plays a crucial role in safeguarding the respiratory tract against microbial infections by serving as the primary site for trapping microbes[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Mucus hypersecretion may result in infection and inflammation in lung injury [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Mucins, produced in the airway epithelia, are the main component of mucus. Therefore, maintaining mucin homeostasis is foundational to the airway health. Excessive expression of inflammatory factors such as IL1β and IL17 can stimulate the transcriptional response of MUCIN genes [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. In the Abx group, the transcription levels of nearly all MUCIN genes, especially MUC4, MUC5B and MUC16, showed a significant upregulation compared to the WT group. Dysregulation of mucins might contribute to uncontrolled inflammation and result in abnormal airway function. Maybe significant clinical symptoms (sneezing and pulmonary crackles) and pathological alteration (interstitial pneumonia) in the Abx group could be interpreted with mucin hyperexpression.\u003c/p\u003e \u003cp\u003eThe proximity and continuity of the nasal cavity and lower respiratory tract allows the nasal microbiome to be a potential determinant of the lung microbiome [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. In our study, the phylogenetic relationship based on the primary microbial Amplicon Sequence Variant (ASV) sequences showed a homology of 100% at genus level, including \u003cem\u003eHaemophilus\u003c/em\u003e, \u003cem\u003eMoraxella\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003ePrevotella_\u003c/em\u003e9, and \u003cem\u003eBifidobacterium\u003c/em\u003e between the lung and the nasal region. During the viral infection, additionally, we observed homogeneous changes in the nasal and lung microbiota. This observation provides additional evidence for the idea that a portion of the lung microbiota is derived from the nasal cavity and that a similar immune response can be induced in the nasal cavity and lung against viral infections. Notably, a previous report has indicated that H7N9 influenza virus infection leads to disruption of the respiratory microbiota and exacerbates bacterial secondary infections [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. However, in our study, we found that infection with influenza virus (H3N2) did not significantly disrupt the nasal and lung microbiota homeostasis. We speculate that the discrepancy may result from different animal models, infection doses, sample collection method, subtypes of influenza virus and environmental factors. Similar findings have been previously reported [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIFNs are crucial mediator of antiviral immunity and homeostatic immune system regulation[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Basal levels of type I IFN production under physiological conditions are maintained by the commensal microbiota [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Previous studies indicate that the depletion of gut microbiota with antibiotics can reduce IFNβ-mediated antiviral activity [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Our study revealed a significant reduction in the transcription levels of ISGs in the nasal and lung tissues following the disruption of nasal microbiota. Therefore, we speculate that disturbance in nasal microbiota, particularly reduced relative abundance of \u003cem\u003eLactobacillus\u003c/em\u003e, may attenuate the host's IFN-mediated antiviral immune response. A recent study has demonstrated that \u003cem\u003eLactobacillus paracasei\u003c/em\u003e modulates lung immunity and enhances the ability to combat influenza virus infection [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Notably, in our study, disruption of nasal microbiota was observed to exacerbate dysbiosis in lung microbiota, followed by increased severity of influenza infection. The Abx group exhibited low levels of ISGs transcription and high levels of inflammatory factor transcription in both nasal and lung compartments post-infection. However, comparison between the Abx and WT groups revealed distinctive transcription patterns of ISGs and inflammatory factors in both nasal and lung tissues. In the nasal compartment, the WT group demonstrated higher levels of ISGs transcription compared to the Abx group, while in the lung tissue, the Abx group exhibited superior transcription levels in inflammatory factors. We speculate that these discrepancies may be attributable to variations in cellular distribution and composition across the two tissues, as well as differences in the composition and relative abundance of nasal and lung microbiota. In future studies, we will employ single-cell sequencing combined with metagenomic analyses to further elucidate the underlying biological disparities. Finally, we isolated ten strains of lactic acid bacteria from the experimental dogs and identified one strain of \u003cem\u003eLactobacillus\u003c/em\u003e with in \u003cem\u003evitro\u003c/em\u003e antiviral activity. This antiviral effect was mediated by the activation of the IFN pathway and the modulation of influenza-induced autophagy inhibition, with cross-talk observed between these two mechanisms. However, the exact mechanisms by which \u003cem\u003eLactobacillus\u003c/em\u003e regulates the interplay between IFN activation and autophagy remain unclear. We hypothesize that \u003cem\u003eLactobacillus\u003c/em\u003e may produce certain substances during the growth, such as short-chain fatty acids (SCFAs), which have been reported to play significant roles in regulating host autophagy and innate immunity[\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Additionally, type I IFNs have been reported to induce autophagy and enhance autophagic flux in human cancer cell lines[\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Further research is needed to elucidate the underlying mechanisms. 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, regarding lung sample collection for sequencing, the samples were obtained from multiple mixed tissue within individuals' lungs. It is likely that microbiome heterogeneity (and thus pneumotype categorization) varies over time and across different segments of the lung. Furthermore, the presence of different cell types across tissues contributes to varied and complex immune responses, and transcriptomic analysis may not accurately reflect the immune reactions of distinct ecological niches. Thirdly, the use of cell models may be insufficient to capture the complexity of the in \u003cem\u003evivo\u003c/em\u003e environment. Therefore, our team is presently enhancing sequencing methodologies and experimental designs to delve into the specific mechanisms and potential biological significance of these findings. 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.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eCollectively, our results indicate that nasal dysbiosis exacerbates microbial dysbiosis of the lungs following viral infection, further diminishing the host's intrinsic antiviral response, amplifying the generation of inflammatory storms, and compromising respiratory barrier integrity. Intriguingly, our study revealed that \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eMoraxella\u003c/em\u003e might exert completely opposing effects in modulating innate immunity and inflammatory responses. Further study will focus on the precise functions of specific bacterial genus or species and the underlying mechanisms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;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).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll raw data used in this study are available in the NCBI Sequence Read Archive (SRA), accession numbers PRJNA1125627, PRJNA1126008.Consent for publication\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eAuthors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eJZ.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. \u0026nbsp;JZ.G. drafted the original paper. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ede Steenhuijsen Piters WAA, Watson RL, de Koff EM, Hasrat R, Arp K, Chu M, et al. Early-life viral infections are associated with disadvantageous immune and microbiota profiles and recurrent respiratory infections. Nat Microbiol. 2022;7(2):224\u0026ndash;37; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41564-021-01043-2\u003c/span\u003e\u003cspan address=\"10.1038/s41564-021-01043-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMan WH, van Houten MA, Merelle ME, Vlieger AM, Chu M, Jansen NJG, et al. Bacterial and viral respiratory tract microbiota and host characteristics in children with lower respiratory tract infections: a matched case-control study. Lancet Respir Med. 2019;7(5):417\u0026ndash;26; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S2213-2600(18)30449-1\u003c/span\u003e\u003cspan address=\"10.1016/S2213-2600(18)30449-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBosch A, de Steenhuijsen Piters WAA, van Houten MA, Chu M, Biesbroek G, Kool J, et al. Maturation of the Infant Respiratory Microbiota, Environmental Drivers, and Health Consequences. A Prospective Cohort Study. Am J Respir Crit Care Med. 2017;196(12):1582\u0026ndash;90; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1164/rccm.201703-0554OC\u003c/span\u003e\u003cspan address=\"10.1164/rccm.201703-0554OC\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLloyd CM, Saglani S. Early-life respiratory infections and developmental immunity determine lifelong lung health. Nat Immunol. 2023;24(8):1234\u0026ndash;43; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41590-023-01550-w\u003c/span\u003e\u003cspan address=\"10.1038/s41590-023-01550-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrismer B, Weidenmaier C, Zipperer A, Peschel A. The commensal lifestyle of Staphylococcus aureus and its interactions with the nasal microbiota. Nat Rev Microbiol. 2017;15(11):675\u0026ndash;87; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrmicro.2017.104\u003c/span\u003e\u003cspan address=\"10.1038/nrmicro.2017.104\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaballero-Flores G, Pickard JM, Nunez G. Microbiota-mediated colonization resistance: mechanisms and regulation. Nat Rev Microbiol. 2023;21(6):347\u0026ndash;60; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41579-022-00833-7\u003c/span\u003e\u003cspan address=\"10.1038/s41579-022-00833-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan AH, Lim SY, Lang AE. The microbiome-gut-brain axis in Parkinson disease - from basic research to the clinic. Nat Rev Neurol. 2022;18(8):476\u0026ndash;95; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41582-022-00681-2\u003c/span\u003e\u003cspan address=\"10.1038/s41582-022-00681-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsu CL, Schnabl B. The gut-liver axis and gut microbiota in health and liver disease. Nat Rev Microbiol. 2023;21(11):719\u0026ndash;33; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41579-023-00904-3\u003c/span\u003e\u003cspan address=\"10.1038/s41579-023-00904-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang F, Lau RI, Liu Q, Su Q, Chan FKL, Ng SC. Gut microbiota in COVID-19: key microbial changes, potential mechanisms and clinical applications. Nat Rev Gastroenterol Hepatol. 2023;20(5):323\u0026ndash;37; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41575-022-00698-4\u003c/span\u003e\u003cspan address=\"10.1038/s41575-022-00698-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Wu Y, Liu J, Yang Y, Li H, Wu X, et al. Differential Oral Microbial Input Determines Two Microbiota Pneumo-Types Associated with Health Status. Adv Sci (Weinh). 2022;9(32):e2203115; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/advs.202203115\u003c/span\u003e\u003cspan address=\"10.1002/advs.202203115\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNatalini JG, Singh S, Segal LN. The dynamic lung microbiome in health and disease. Nat Rev Microbiol. 2023;21(4):222\u0026ndash;35; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41579-022-00821-x\u003c/span\u003e\u003cspan address=\"10.1038/s41579-022-00821-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWypych TP, Wickramasinghe LC, Marsland BJ. The influence of the microbiome on respiratory health. Nat Immunol. 2019;20(10):1279\u0026ndash;90; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41590-019-0451-9\u003c/span\u003e\u003cspan address=\"10.1038/s41590-019-0451-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu W, Xu J, Pi Z, Chen Y, Jiang G, Wan Y, et al. Untangling the web of intratumor microbiota in lung cancer. Biochim Biophys Acta Rev Cancer. 2023;1878(6):189025; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bbcan.2023.189025\u003c/span\u003e\u003cspan address=\"10.1016/j.bbcan.2023.189025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eProctor DM, Relman DA. The Landscape Ecology and Microbiota of the Human Nose, Mouth, and Throat. Cell Host Microbe. 2017;21(4):421\u0026ndash;32; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chom.2017.03.011\u003c/span\u003e\u003cspan address=\"10.1016/j.chom.2017.03.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang W, Yang Y, Gong S, Wei M, Ma Y, Feng R, et al. Airway dysbiosis accelerates lung function decline in chronic obstructive pulmonary disease. \u003cem\u003eCell Host Microbe\u003c/em\u003e. 2023;31(6):1054-70 e9; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chom.2023.04.018\u003c/span\u003e\u003cspan address=\"10.1016/j.chom.2023.04.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu BG, Sulaiman I, Tsay JJ, Perez L, Franca B, Li Y, et al. Episodic Aspiration with Oral Commensals Induces a MyD88-dependent, Pulmonary T-Helper Cell Type 17 Response that Mitigates Susceptibility to Streptococcus pneumoniae. Am J Respir Crit Care Med. 2021;203(9):1099\u0026ndash;111; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1164/rccm.202005-1596OC\u003c/span\u003e\u003cspan address=\"10.1164/rccm.202005-1596OC\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorn KJ, Schopper MA, Drigot ZG, Clark SE. Airway Prevotella promote TLR2-dependent neutrophil activation and rapid clearance of Streptococcus pneumoniae from the lung. Nat Commun. 2022;13(1):3321; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-022-31074-0\u003c/span\u003e\u003cspan address=\"10.1038/s41467-022-31074-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosas-Salazar C, Kimura KS, Shilts MH, Strickland BA, Freeman MH, Wessinger BC, et al. SARS-CoV-2 infection and viral load are associated with the upper respiratory tract microbiome. \u003cem\u003eJ Allergy Clin Immunol\u003c/em\u003e. 2021;147(4):1226-33 e2; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jaci.2021.02.001\u003c/span\u003e\u003cspan address=\"10.1016/j.jaci.2021.02.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEderveen THA, Ferwerda G, Ahout IM, Vissers M, de Groot R, Boekhorst J, et al. Haemophilus is overrepresented in the nasopharynx of infants hospitalized with RSV infection and associated with increased viral load and enhanced mucosal CXCL8 responses. Microbiome. 2018;6(1):10; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40168-017-0395-y\u003c/span\u003e\u003cspan address=\"10.1186/s40168-017-0395-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee KH, Gordon A, Shedden K, Kuan G, Ng S, Balmaseda A, et al. The respiratory microbiome and susceptibility to influenza virus infection. PLoS One. 2019;14(1):e0207898; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0207898\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0207898\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGroeger D, Schiavi E, Grant R, Kurnik-Lucka M, Michalovich D, Williamson R, et al. Intranasal Bifidobacterium longum protects against viral-induced lung inflammation and injury in a murine model of lethal influenza infection. EBioMedicine. 2020;60:102981; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ebiom.2020.102981\u003c/span\u003e\u003cspan address=\"10.1016/j.ebiom.2020.102981\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin Y, Xie X, Zhao Y, Kalhoro DH, Lu C, Liu Y. Enhanced replication of avian-origin H3N2 canine influenza virus in eggs, cell cultures and mice by a two-amino acid insertion in neuraminidase stalk. Vet Res. 2016;47(1):53; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13567-016-0337-x\u003c/span\u003e\u003cspan address=\"10.1186/s13567-016-0337-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang YM, Kim HM, Ku KB, Park EH, Yum J, Seo SH. H3N2 canine influenza virus causes severe morbidity in dogs with induction of genes related to inflammation and apoptosis. Vet Res. 2013;44(1):92; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1297-9716-44-92\u003c/span\u003e\u003cspan address=\"10.1186/1297-9716-44-92\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCuthbertson L, Craven V, Bingle L, Cookson W, Everard ML, Moffatt MF. The impact of persistent bacterial bronchitis on the pulmonary microbiome of children. PLoS One. 2017;12(12):e0190075; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0190075\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0190075\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHabibi MS, Thwaites RS, Chang M, Jozwik A, Paras A, Kirsebom F, et al. Neutrophilic inflammation in the respiratory mucosa predisposes to RSV infection. Science. 2020;370(6513); doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science.aba9301\u003c/span\u003e\u003cspan address=\"10.1126/science.aba9301\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu YX, Qin Y, Chen T, Lu M, Qian X, Guo X, et al. A practical guide to amplicon and metagenomic analysis of microbiome data. Protein Cell. 2021;12(5):315\u0026ndash;30; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s13238-020-00724-8\u003c/span\u003e\u003cspan address=\"10.1007/s13238-020-00724-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26(19):2460\u0026ndash;1; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btq461\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btq461\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRognes T, Flouri T, Nichols B, Quince C, Mah\u0026eacute; F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7717/peerj.2584\u003c/span\u003e\u003cspan address=\"10.7717/peerj.2584\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOksanen J, Kindt R, Legendre P, O\u0026rsquo;Hara B, Stevens MHH, Oksanen MJ, et al. The vegan package. Community ecology package. 2007;10(631\u0026ndash;637):719.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLove MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13059-014-0550-8\u003c/span\u003e\u003cspan address=\"10.1186/s13059-014-0550-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnow GL. ggplot2: Elegant Graphics for Data Analysis. The American statistician. 2011;(3):65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, et al. Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012;9(7):676\u0026ndash;82; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nmeth.2019\u003c/span\u003e\u003cspan address=\"10.1038/nmeth.2019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics. 2012;16(5):284\u0026ndash;7; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/omi.2011.0118\u003c/span\u003e\u003cspan address=\"10.1089/omi.2011.0118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuadros RM, Miura H, Harms DW, Akatsuka H, Sato T, Aida T, et al. Easi-CRISPR: a robust method for one-step generation of mice carrying conditional and insertion alleles using long ssDNA donors and CRISPR ribonucleoproteins. Genome Biol. 2017;18(1):92; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13059-017-1220-4\u003c/span\u003e\u003cspan address=\"10.1186/s13059-017-1220-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu W, Winter MG, Byndloss MX, Spiga L, Duerkop BA, Hughes ER, et al. Precision editing of the gut microbiota ameliorates colitis. Nature. 2018;553(7687):208\u0026ndash;11; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nature25172\u003c/span\u003e\u003cspan address=\"10.1038/nature25172\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDouglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR, Taylor CM, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020;38(6):685\u0026ndash;88; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41587-020-0548-6\u003c/span\u003e\u003cspan address=\"10.1038/s41587-020-0548-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePowers JH, 3rd, Bacci ED, Guerrero ML, Leidy NK, Stringer S, Kim K, et al. Reliability, Validity, and Responsiveness of InFLUenza Patient-Reported Outcome (FLU-PRO\u0026copy;) Scores in Influenza-Positive Patients. Value Health. 2018;21(2):210\u0026ndash;18; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jval.2017.04.014\u003c/span\u003e\u003cspan address=\"10.1016/j.jval.2017.04.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Jonge J, Isakova-Sivak I, van Dijken H, Spijkers S, Mouthaan J, de Jong R, et al. H7N9 Live Attenuated Influenza Vaccine Is Highly Immunogenic, Prevents Virus Replication, and Protects Against Severe Bronchopneumonia in Ferrets. Mol Ther. 2016;24(5):991\u0026ndash;1002; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/mt.2016.23\u003c/span\u003e\u003cspan address=\"10.1038/mt.2016.23\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Steenhuijsen Piters WAA, Binkowska J, Bogaert D. Early Life Microbiota and Respiratory Tract Infections. Cell Host Microbe. 2020;28(2):223\u0026ndash;32; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chom.2020.07.004\u003c/span\u003e\u003cspan address=\"10.1016/j.chom.2020.07.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChatterjee M, van Putten JPM, Strijbis K. Defensive Properties of Mucin Glycoproteins during Respiratory Infections-Relevance for SARS-CoV-2. mBio. 2020;11(6); doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/mBio.02374-20\u003c/span\u003e\u003cspan address=\"10.1128/mBio.02374-20\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRadicioni G, Ceppe A, Ford AA, Alexis NE, Barr RG, Bleecker ER, et al. Airway mucin MUC5AC and MUC5B concentrations and the initiation and progression of chronic obstructive pulmonary disease: an analysis of the SPIROMICS cohort. Lancet Respir Med. 2021;9(11):1241\u0026ndash;54; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S2213-2600(21)00079-5\u003c/span\u003e\u003cspan address=\"10.1016/S2213-2600(21)00079-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePontarollo G, Kollar B, Mann A, Khuu MP, Kiouptsi K, Bayer F, et al. Commensal bacteria weaken the intestinal barrier by suppressing epithelial neuropilin-1 and Hedgehog signaling. Nat Metab. 2023;5(7):1174\u0026ndash;87; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s42255-023-00828-5\u003c/span\u003e\u003cspan address=\"10.1038/s42255-023-00828-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, Han C, Li T, Li N, Cao X. The methyltransferase PRMT6 attenuates antiviral innate immunity by blocking TBK1-IRF3 signaling. Cell Mol Immunol. 2019;16(10):800\u0026ndash;09; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41423-018-0057-4\u003c/span\u003e\u003cspan address=\"10.1038/s41423-018-0057-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo X, Zhang Z, Lin C, Ren H, Li Y, Zhang Y, et al. A/(H1N1) pdm09 NS1 promotes viral replication by enhancing autophagy through hijacking the IAV negative regulatory factor LRPPRC. Autophagy. 2023;19(5):1533\u0026ndash;50; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/15548627.2022.2139922\u003c/span\u003e\u003cspan address=\"10.1080/15548627.2022.2139922\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeale R, Wise H, Stuart A, Ravenhill BJ, Digard P, Randow F. A LC3-interacting motif in the influenza A virus M2 protein is required to subvert autophagy and maintain virion stability. Cell Host Microbe. 2014;15(2):239\u0026ndash;47; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chom.2014.01.006\u003c/span\u003e\u003cspan address=\"10.1016/j.chom.2014.01.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMunz C. Influenza A virus lures autophagic protein LC3 to budding sites. Cell Host Microbe. 2014;15(2):130\u0026ndash;1; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chom.2014.01.014\u003c/span\u003e\u003cspan address=\"10.1016/j.chom.2014.01.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiranda A, Shirley CA, Jenkins RW. Emerging roles of TBK1 in cancer immunobiology. Trends Cancer. 2024; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.trecan.2024.02.007\u003c/span\u003e\u003cspan address=\"10.1016/j.trecan.2024.02.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu Z, Xie Y, Lu J, Yang J, Zhang J, Jiang H, et al. VANGL2 inhibits antiviral IFN-I signaling by targeting TBK1 for autophagic degradation. Sci Adv. 2023;9(25):eadg2339; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/sciadv.adg2339\u003c/span\u003e\u003cspan address=\"10.1126/sciadv.adg2339\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGamblin SJ, Skehel JJ. Influenza hemagglutinin and neuraminidase membrane glycoproteins. J Biol Chem. 2010;285(37):28403\u0026ndash;9; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1074/jbc.R110.129809\u003c/span\u003e\u003cspan address=\"10.1074/jbc.R110.129809\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePillarisetti N, Broderick D, Ainsworth A, Mulholland A, Wagner Mackenzie B, Middleton D, et al. The airway microbiota in children newly diagnosed with bronchiectasis largely retains its diversity. Eur Respir J. 2019;54(2); doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1183/13993003.00704-2019\u003c/span\u003e\u003cspan address=\"10.1183/13993003.00704-2019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim JG, Zhang A, Rauseo AM, Goss CW, Mudd PA, O'Halloran JA, et al. The salivary and nasopharyngeal microbiomes are associated with SARS-CoV-2 infection and disease severity. J Med Virol. 2023;95(2):e28445; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jmv.28445\u003c/span\u003e\u003cspan address=\"10.1002/jmv.28445\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao X, Jiang L, Fang X, Guo Z, Wang X, Shi B, et al. Host-microbiota interaction-mediated resistance to inflammatory bowel disease in pigs. Microbiome. 2022;10(1):115; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40168-022-01303-1\u003c/span\u003e\u003cspan address=\"10.1186/s40168-022-01303-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan HT, Hagner S, Ruchti F, Radzikowska U, Tan G, Altunbulakli C, et al. Tight junction, mucin, and inflammasome-related molecules are differentially expressed in eosinophilic, mixed, and neutrophilic experimental asthma in mice. Allergy. 2019;74(2):294\u0026ndash;307; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/all.13619\u003c/span\u003e\u003cspan address=\"10.1111/all.13619\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCostain G, Liu Z, Mennella V, Radicioni G, Goczi AN, Albulescu A, et al. Hereditary Mucin Deficiency Caused by Biallelic Loss of Function of MUC5B. Am J Respir Crit Care Med. 2022;205(7):761\u0026ndash;68; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1164/rccm.202106-1456OC\u003c/span\u003e\u003cspan address=\"10.1164/rccm.202106-1456OC\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang B. Mucins produced by type II pneumocyte: culprits in SARS-CoV-2 pathogenesis. Cell Mol Immunol. 2021;18(7):1823\u0026ndash;25; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41423-021-00714-8\u003c/span\u003e\u003cspan address=\"10.1038/s41423-021-00714-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu H, Cao C, Wu Z, Zhang H, Sun Z, Wang M, et al. The probiotic L. casei Zhang slows the progression of acute and chronic kidney disease. \u003cem\u003eCell Metab\u003c/em\u003e. 2021;33(10):1926-42 e8; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cmet.2021.06.014\u003c/span\u003e\u003cspan address=\"10.1016/j.cmet.2021.06.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Y, Liu Y, Wang Y, Chen X, Wang C, Chen X, et al. Prevotellaceae produces butyrate to alleviate PD-1/PD-L1 inhibitor-related cardiotoxicity via PPARalpha-CYP4X1 axis in colonic macrophages. J Exp Clin Cancer Res. 2022;41(1):1; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13046-021-02201-4\u003c/span\u003e\u003cspan address=\"10.1186/s13046-021-02201-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJing Y, Yang D, Bai F, Wang Q, Zhang C, Yan Y, et al. Spinal cord injury-induced gut dysbiosis influences neurological recovery partly through short-chain fatty acids. NPJ Biofilms Microbiomes. 2023;9(1):99; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41522-023-00466-5\u003c/span\u003e\u003cspan address=\"10.1038/s41522-023-00466-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeleu S, Machiels K, Raes J, Verbeke K, Vermeire S. Short chain fatty acids and its producing organisms: An overlooked therapy for IBD? EBioMedicine. 2021;66:103293; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ebiom.2021.103293\u003c/span\u003e\u003cspan address=\"10.1016/j.ebiom.2021.103293\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang RX, Lee JS, Campbell EL, Colgan SP. Microbiota-derived butyrate dynamically regulates intestinal homeostasis through regulation of actin-associated protein synaptopodin. Proc Natl Acad Sci U S A. 2020;117(21):11648\u0026ndash;57; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.1917597117\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1917597117\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrompette A, Gollwitzer ES, Pattaroni C, Lopez-Mejia IC, Riva E, Pernot J, et al. Dietary Fiber Confers Protection against Flu by Shaping Ly6c(-) Patrolling Monocyte Hematopoiesis and CD8(+) T Cell Metabolism. Immunity. 2018;48(5):992\u0026ndash;1005.e8; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.immuni.2018.04.022\u003c/span\u003e\u003cspan address=\"10.1016/j.immuni.2018.04.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeeney DD, Zhai Z, Bendiks Z, Barouei J, Martinic A, Slupsky C, et al. Lactobacillus plantarum bacteriocin is associated with intestinal and systemic improvements in diet-induced obese mice and maintains epithelial barrier integrity in vitro. Gut Microbes. 2019;10(3):382\u0026ndash;97; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/19490976.2018.1534513\u003c/span\u003e\u003cspan address=\"10.1080/19490976.2018.1534513\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamsheh MY, Haldar K, Esteve-Codina A, Purser LF, Richardson M, M\u0026uuml;ller-Quernheim J, et al. Lung microbiome composition and bronchial epithelial gene expression in patients with COPD versus healthy individuals: a bacterial 16S rRNA gene sequencing and host transcriptomic analysis. Lancet Microbe. 2021;2(7):e300-e10; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s2666-5247(21)00035-5\u003c/span\u003e\u003cspan address=\"10.1016/s2666-5247(21)00035-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSulaiman I, Wu BG, Chung M, Isaacs B, Tsay JJ, Holub M, et al. Lower Airway Dysbiosis Augments Lung Inflammatory Injury in Mild-to-Moderate Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med. 2023;208(10):1101\u0026ndash;14; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1164/rccm.202210-1865OC\u003c/span\u003e\u003cspan address=\"10.1164/rccm.202210-1865OC\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlevogt H, Seybold J, Tiwari KN, Hocke AC, Jonatat C, Dietel S, et al. Moraxella catarrhalis is internalized in respiratory epithelial cells by a trigger-like mechanism and initiates a TLR2- and partly NOD1-dependent inflammatory immune response. Cell Microbiol. 2007;9(3):694\u0026ndash;707; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1462-5822.2006.00821.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1462-5822.2006.00821.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBezemer GF, Sagar S, van Bergenhenegouwen J, Georgiou NA, Garssen J, Kraneveld AD, et al. Dual role of Toll-like receptors in asthma and chronic obstructive pulmonary disease. Pharmacol Rev. 2012;64(2):337\u0026ndash;58; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1124/pr.111.004622\u003c/span\u003e\u003cspan address=\"10.1124/pr.111.004622\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKomarova YA, Kruse K, Mehta D, Malik AB. Protein Interactions at Endothelial Junctions and Signaling Mechanisms Regulating Endothelial Permeability. Circ Res. 2017;120(1):179\u0026ndash;206; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/circresaha.116.306534\u003c/span\u003e\u003cspan address=\"10.1161/circresaha.116.306534\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZanin M, Baviskar P, Webster R, Webby R. The Interaction between Respiratory Pathogens and Mucus. Cell Host Microbe. 2016;19(2):159\u0026ndash;68; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chom.2016.01.001\u003c/span\u003e\u003cspan address=\"10.1016/j.chom.2016.01.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRogers DF. Physiology of airway mucus secretion and pathophysiology of hypersecretion. Respir Care. 2007;52(9):1134\u0026ndash;46; discussion 46\u0026thinsp;\u0026ndash;\u0026thinsp;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBassis CM, Erb-Downward JR, Dickson RP, Freeman CM, Schmidt TM, Young VB, et al. Analysis of the upper respiratory tract microbiotas as the source of the lung and gastric microbiotas in healthy individuals. mBio. 2015;6(2):e00037; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/mBio.00037-15\u003c/span\u003e\u003cspan address=\"10.1128/mBio.00037-15\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDickson RP, Erb-Downward JR, Huffnagle GB. Homeostasis and its disruption in the lung microbiome. Am J Physiol Lung Cell Mol Physiol. 2015;309(10):L1047-55; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1152/ajplung.00279.2015\u003c/span\u003e\u003cspan address=\"10.1152/ajplung.00279.2015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVentero MP, Moreno-Perez O, Molina-Pardines C, Paytuv\u0026iacute;-Gallart A, Boix V, Escribano I, et al. Nasopharyngeal Microbiota as an early severity biomarker in COVID-19 hospitalised patients. J Infect. 2022;84(3):329\u0026ndash;36; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jinf.2021.12.030\u003c/span\u003e\u003cspan address=\"10.1016/j.jinf.2021.12.030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHau SJ, Nielsen DW, Mou KT, Alt DP, Kellner S, Brockmeier SL. Resilience of swine nasal microbiota to influenza A virus challenge in a longitudinal study. Vet Res. 2023;54(1):38; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13567-023-01167-9\u003c/span\u003e\u003cspan address=\"10.1186/s13567-023-01167-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing T, Song T, Zhou B, Geber A, Ma Y, Zhang L, et al. Microbial Composition of the Human Nasopharynx Varies According to Influenza Virus Type and Vaccination Status. mBio. 2019;10(4); doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/mBio.01296-19\u003c/span\u003e\u003cspan address=\"10.1128/mBio.01296-19\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStefan KL, Kim MV, Iwasaki A, Kasper DL. Commensal Microbiota Modulation of Natural Resistance to Virus Infection. Cell. 2020;183(5):1312-24.e10; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cell.2020.10.047\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2020.10.047\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbt MC, Osborne LC, Monticelli LA, Doering TA, Alenghat T, Sonnenberg GF, et al. Commensal bacteria calibrate the activation threshold of innate antiviral immunity. Immunity. 2012;37(1):158\u0026ndash;70; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.immuni.2012.04.011\u003c/span\u003e\u003cspan address=\"10.1016/j.immuni.2012.04.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim S, Lee S, Kim TY, Lee SH, Seo SU, Kweon MN. Newly isolated Lactobacillus paracasei strain modulates lung immunity and improves the capacity to cope with influenza virus infection. Microbiome. 2023;11(1):260; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40168-023-01687-8\u003c/span\u003e\u003cspan address=\"10.1186/s40168-023-01687-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang Y, Chen Y, Jiang H, Nie D. Short-chain fatty acids induced autophagy serves as an adaptive strategy for retarding mitochondria-mediated apoptotic cell death. Cell Death Differ. 2011;18(4):602\u0026ndash;18; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/cdd.2010.117\u003c/span\u003e\u003cspan address=\"10.1038/cdd.2010.117\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang G, Liu J, Zhang Y, Xie J, Chen S, Shi Y, et al. Ginsenoside Rg3 enriches SCFA-producing commensal bacteria to confer protection against enteric viral infection via the cGAS-STING-type I IFN axis. ISME J. 2023;17(12):2426\u0026ndash;40; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41396-023-01541-7\u003c/span\u003e\u003cspan address=\"10.1038/s41396-023-01541-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmbjorn M, Ejlerskov P, Liu Y, Lees M, Jaattela M, Issazadeh-Navikas S. IFNB1/interferon-beta-induced autophagy in MCF-7 breast cancer cells counteracts its proapoptotic function. Autophagy. 2013;9(3):287\u0026ndash;302; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4161/auto.22831\u003c/span\u003e\u003cspan address=\"10.4161/auto.22831\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mbio","sideBox":"Learn more about [Microbiome](http://microbiomejournal.biomedcentral.com/)","snPcode":"40168","submissionUrl":"https://submission.nature.com/new-submission/40168/3","title":"Microbiome","twitterHandle":"@MicrobiomeJ","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Influenza, Respiratory microbiome, IFN, Autophagy","lastPublishedDoi":"10.21203/rs.3.rs-4612057/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4612057/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe respiratory tract houses a specialized microbial ecosystem, and despite the close anatomical and physiological ties between the oral, upper respiratory, and lower respiratory tracts, there is a substantial discrepancy in microbial quantity, spanning multiple orders of magnitude. The potential for commensal bacteria to prevent infection lies in their ability to regulate innate and adaptive host immune responses. Influenza virus predominantly targets and replicates within the epithelial cells of both upper and lower respiratory tracts. Given this, we hypothesize that the nasal-lung-microbe cross-talk plays a crucial role in influencing influenza susceptibility. In this study, we investigated viral presence, gene expression profiles of host, and the nasal and lung microbiota in a beagle dog model with antibiotic-induced nasal dysbiosis during influenza virus infection.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn this study, using 16S rRNA sequencing, combined with comparative anatomy, transcriptomics and histological examination, we investigated viral presence, gene expression profiles of host, and the nasal and lung microbiota in influenza-infected 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 worsens the influenza-induced disturbance in lung microbiota. Further, we identified one strain of \u003cem\u003eLactobacillus plantarum\u003c/em\u003e with a significant antiviral effect, which is exerted by activating the IFN pathway and modulating the impaired autophagy flux induced by influenza virus. Our data collectively indicate a close connection between the microbiomes of different ecological niches in the nasal and lung regions. This connection significantly influences subsequent host-microbe cross-talk, which was associated with an increased susceptibility to influenza.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur investigation reveals that nasal microbiota dysbiosis not only increases host susceptibility to influenza virus infection but also contributes to the exacerbation of influenza-induced lung microbiota dysregulation. This intricate relationship extends to the microbiome composition, demonstrating correlations with critical factors such as host antiviral responses, inflammation thresholds, and mucosal barrier integrity. Together, these findings underscore the substantial impact of nasal microbiota dysbiosis on the overall outcome during influenza infections.\u003c/p\u003e","manuscriptTitle":"Nasal microbiota homeostasis regulates host anti-influenza immunity via the IFN and autophagy pathways in beagles","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-16 13:46:10","doi":"10.21203/rs.3.rs-4612057/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-01T12:49:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-01T12:39:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-24T14:39:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microbiome","date":"2024-06-20T13:22:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mbio","sideBox":"Learn more about [Microbiome](http://microbiomejournal.biomedcentral.com/)","snPcode":"40168","submissionUrl":"https://submission.nature.com/new-submission/40168/3","title":"Microbiome","twitterHandle":"@MicrobiomeJ","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"36088684-0a2c-4c45-a6ef-4fcf98983089","owner":[],"postedDate":"July 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-03T16:02:15+00:00","versionOfRecord":{"articleIdentity":"rs-4612057","link":"https://doi.org/10.1186/s40168-025-02031-y","journal":{"identity":"microbiome","isVorOnly":false,"title":"Microbiome"},"publishedOn":"2025-01-27 15:57:31","publishedOnDateReadable":"January 27th, 2025"},"versionCreatedAt":"2024-07-16 13:46:10","video":"","vorDoi":"10.1186/s40168-025-02031-y","vorDoiUrl":"https://doi.org/10.1186/s40168-025-02031-y","workflowStages":[]},"version":"v1","identity":"rs-4612057","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4612057","identity":"rs-4612057","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}