Akkermansia muciniphila Enhances Resistance to Infectious Bronchitis Virus in Chickens Through γ-Aminobutyric Acid-Mediated Anti-Inflammatory Pathways

Akkermansia muciniphila Enhances Resistance to Infectious Bronchitis Virus in Chickens Through γ-Aminobutyric Acid-Mediated Anti-Inflammatory Pathways | 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 Akkermansia muciniphila Enhances Resistance to Infectious Bronchitis Virus in Chickens Through γ-Aminobutyric Acid-Mediated Anti-Inflammatory Pathways Ouyang Peng, Yufang Liu, Xuanci Wang, Yihui Huang, Yiqin Yang, and 18 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6786224/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Infectious bronchitis virus (IBV) is a major viral pathogen causing substantial economic losses in the global poultry industry. However, the limited efficacy of commercial vaccines and the absence of approved antiviral drugs underscore the urgent need for novel strategies to combat IBV, particularly in the context of antibiotic-free poultry production. Emerging evidences suggest that gut microbiota plays a crucial role in shaping host immunity and antiviral resilience. Results In this study, we demonstrated that gut microbiota composition is a key determinant of IBV resistance in chickens, as revealed through a fecal microbiota transplantation (FMT) model. Utilizing multi-omics approaches, we conducted comprehensive characterization of microbiomic, viromic, and metabolic differences among adult chickens, young chickens, and FMT-treated young chickens for the first time, establishing a mechanistic link between gut microbiota and IBV resistance. Building on these insights, longitudinal microbiome profiling in a newly developed persistent IBV infection model identified Akkermansia muciniphila as the key bacterium conferring IBV resistance, with its administration improving survival rates from 30–35% to 90–95%. Additionally, two novel candidate probiotics, Cloacibacillus porcorum and Neglecta timonensis , exhibited moderate yet measurable protective effects. Mechanistic investigations revealed that Akkermansia muciniphila enhances γ-aminobutyric acid (GABA) production, which suppresses NF-κB-driven inflammatory responses, reduces pro-inflammatory cytokine levels, alleviates nephritis, and upregulates antiviral interferon expression, thereby fortifying host defenses against IBV infection. Conclusions These findings provide a scientific foundation for deploying live biotherapeutic products (LBPs) as an intervention strategy against IBV and highlight the broader potential of gut microbiota modulation in mitigating infectious diseases and optimizing poultry health management. Infectious bronchitis virus (IBV) antibiotic-free farming gut microbiota fecal microbiota transplantation (FMT) Akkermansia muciniphila antiviral mechanistic investigation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background The gut microbiota plays an essential role in maintaining host health, exerting its influence on host physiology through diverse and multifaceted mechanisms [1, 2]. A key feature of the gut microbiota is its core functional groups, which exist in a dynamic and reciprocal equilibrium with host and environmental factors [3]. Dysbiosis of the gut microbiota has been implicated in the onset and progression of several diseases, including Crohn’s disease, type 2 diabetes, colorectal cancer [4-7], as well as viral infections such as COVID-19 and influenza [8, 9]. Conversely, deciphering the complex interactions between the gut microbiota and the host has paved the way for novel therapeutic interventions targeting specific microbial populations [10-12]. For example, microbial metabolites such as short-chain fatty acids (SCFAs) can enhance the immune responses of CD8 + T cells [13] and have the potential to benefit animal bone health [14]. Large-scale clinical trials have demonstrated that probiotics can restore gut microbiota homeostasis and alleviate post-COVID-19 syndrome [15]. These findings underscore the transformative potential of leveraging microbiota-host interactions to develop innovative strategies for preventing and treating diseases with limited control measures. The gut microbiome has also been linked to the growth performance, development, and overall health of domestic avian species such as chickens and ducks [16, 17]. However, the specific contributions of gut microbiota to the pathogenesis and progression of poultry diseases remain largely unexplored. In China, the yellow-feathered broilers represent a major poultry breed, with an annual production volume approaching 4 billion birds [18]. Nonetheless, despite their economic importance, these birds remain highly susceptible to infectious bronchitis virus (IBV) [19]. IBV is transmitted primarily through aerosols, exhibits broad tissue tropism, and induces severe disease with high mortality rates in young chickens and reproductive tract damage in adult chickens [19, 20]. As a result, IBV inflicts annual economic losses amounting to millions of dollars and remains one of the most devastating pathogens threatening the global poultry industry [21]. Like other coronaviruses, IBV is characterized by a high mutation rate, strong inflammatory induction, and immune evasion strategies [22-24]. Moreover, its extensive genetic and serotypic diversity complicates vaccine development, as existing vaccines often fail to provide cross-protection against emerging variants, leading to frequent immune failures [25-27]. At present, no approved therapeutics are available for the effective treatment of IBV in clinical settings [28], emphasizing the urgent need for innovative and effective control strategies. Recent studies suggest that modulating gut microbiota represents a promising avenue for combating viral infections, including those caused by SARS-CoV-2 and flaviviruses [15, 29]. Despite these advancements, the role of gut microbiota in mitigating the transmission and infection of IBV remains insufficiently explored. In this study, we applied multi-omics approaches, integrating microbiomics, viromics, and untargeted metabolomics, to elucidate the gut microbiota-mediated mechanisms underlying why adult yellow-feathered chickens (60-70 days) exhibit higher resistance to IBV compared to young yellow-feathered chickens (10-20 days). Our findings identified Akkermansia muciniphila ( A. muciniphila ) as the key microbial species conferring IBV resistance, with Cloacibacillus porcorum and Neglecta timonensis exhibiting more limited but supportive roles. Using in vivo experiments, we demonstrated that A. muciniphila significantly improves IBV resistance in young chickens. Furthermore, mechanistic investigation revealed that A. muciniphila exerts its protective effects by attenuating inflammation through the GABA-NF-κB signaling pathway and stimulating antiviral interferon production, ultimately boosting survival rates. Collectively, these findings provide a mechanistic basis for microbiota-mediated IBV resistance and introduce a novel live biotherapeutic products (LBPs)-based strategy for IBV prevention and mitigation, with broader implications for the development of therapeutics against other viral diseases. Methods Clinical samples and experimental animals Clinical samples from diseased chickens were obtained from poultry farm owners cooperated with Wen’s Food Group and Harbin Veterinary Research Institute (HVRI). Samples, including trachea, kidneys, and other diseased tissues, were collected from diseased or dead chickens and stored at −80 °C until use. For in vivo experiments, yellow-feathered broilers were purchased from collaborating farmers of Wen’s Food Group, and specific pathogen-free (SPF) chickens were obtained from Guangdong Wens Dahuanong Biotechnology Co., Ltd. Infectious bronchitis virus (IBV) propagation and challenge model The QX-type IBV strain H24 was isolated and stored in our laboratory. IBV was propagated in 9- to 11-day-old SPF embryonated chicken eggs at 37 °C with 65% humidity. Inoculated eggs were monitored daily by candling for embryonic mortality. After 48 hours, the allantoic fluid was harvested, and the virus titer was determined using the 50% egg infective dose (EID 50 ) method as described previously [30]. For the conventional infection model, the IBV challenge dose was 10 5 EID 50 , while in the low-dose infection model, the IBV challenge dose was 10 3 EID 50 . Preparation and transplantation of chicken gut microbiota suspension To investigate the influence of adult and young chicken gut microbiota on IBV resistance in yellow-feathered broilers, fresh feces were collected from adult chickens (60-70 days old) and young chickens (10-20 days old). The microbiota suspension was prepared under anaerobic conditions as described previously [31]. Feces were homogenized and diluted with sterile saline, then filtered through sieves of 10, 18, 35, and 60 mesh. The filtrate was centrifuged at 6000 × g for 15 minutes, and the pellet was resuspended in sterile saline with 20% glycerol to a final concentration of 1 × 10⁹ CFU/mL. Two hundred 1-day-old SPF chickens were randomly divided into five groups (40 chickens per group: 20 for mortality observation and 20 for sample collection). The Abx-IBV, Abx-FMT(A)-IBV, and Abx-FMT(Y)-IBV groups were treated with antibiotics (ABX) in drinking water for five consecutive days. ABX doses were: vancomycin (80 mg/kg), neomycin (300 mg/kg), metronidazole (200 mg/kg), ampicillin (0.2 mg/kg), and colistin (24 mg/kg), with water changed every two days. One day after ABX cessation, the Abx-FMT(A)-IBV and Abx-FMT(Y)-IBV groups received 0.2 mL of fecal suspension from adult and young chickens, respectively, via oral gavage. This transplantation was repeated after three days, and IBV infection was conducted one day later. Collection of fecal samples for multi-omics sequencing To analyze gut microenvironment differences among adult (Adult), young (Young), and young chickens transplanted with adult microbiota (Young-FMT), fresh fecal samples were collected from 60 adult chickens, 60 young chickens, and 60 Young-FMT chickens. For each group, samples from three chickens were pooled to reduce individual variation, resulting in 20 replicates per group. Samples were immediately frozen in liquid nitrogen and stored at −80 °C for 16S rRNA gene, viromics, and untargeted metabolomics sequencing. Analysis pipeline for 16S rRNA gene amplicon sequencing The first step is to use iTools (v.0.25) for quality control of the raw data. Reads that matched primers were selected by cutadapt v2.6 software to remove primer and joint contamination, and fragments of the target region were obtained. Then, removing reads that are contaminated by adapter sequences. Finally, readfq (v1.0) was used to remove reads with ambiguous base and low-complexity reads. The second step is to connect Tags and OTU clustering. The first approach utilizes Usearch, which clusters sequences based on 97% similarity to generate operational taxonomic units (OTUs). When using the Usearch method, paired-end reads with overlapping regions are assembled into a consensus sequence using FLASH (v1.2.11). The assembly requires a minimum overlapping length of 15 bp and allows a maximum mismatching ratio of 0.1 in the overlapping region. Tags are clustered into OTU with a 97% threshold by UPARSE, where the unique OTU representative sequences can be obtained. Then, chimeras in OTU are screened and filtered by mapping to gold database (v20110519) by UCHIME (v4.2.40). All tags are mapped to OTU representative sequences using USEARCH GLOBAL to calculate OTU abundance table. The second approach employs DADA2, which generates amplicon sequence variants (ASVs) by clustering denoised sequences with 100% similarity. The filtered paired-end sequences are imported using the qiime tools import command. Then, the qiime dada2 denoise-paired command is used to construct the feature table of the imported paired-end sequences based on DADA2. Finally, the feature table is converted into a directly viewable format using qiime tools export. The third step is to annotate OTU taxonomy. OTU representative sequences are aligned against the database for taxonomic annotation by RDP classifer (v2.2) software (sequence identity is set to be 0.6). Then removing OTU that are not annotated and those taxonomies that do not match with the project’s research background. In the fourth step, mothur (v.1.31.2) was used to analyze the Alpha diversity of species in a single sample, and the parameters included Chao1 index, Ace index, Shannon index, Simpson index, etc. The fifth step is to use QIIME (v1.80) for Beta diversity analysis. The fifth step is to use PICRUST2 to predict the function of bacterial community. Linear discriminant analysis effect size (LEfSe) analysis LEfSe analysis was performed on the platform Galaxy (https://usegalaxy.org/). Select the structure of the problem (classes, subclasses, and subjects) and format the tabular abundance data. Perform the analysis using the formatted tabular abundance data. Graphically report the discovered biomarkers along with their effect sizes. Represent the discovered biomarkers in a taxonomic tree based on hierarchical feature names. Plot the raw values of a single feature as an abundance histogram structured by classes and subclasses. Plot the raw values of all features as abundance histograms structured by classes and subclasses, and provide a zip archive containing the figures. Meta-transcriptomic virome analysis Hecatomb is used for meta-transcriptomic virome analysis [32]. The analytical process begins with stringent preprocessing to ensure that only non-contaminant biological sequences are retained for downstream analysis. This includes the removal of non-biological sequences such as primers and adapters, host sequence depletion, redundancy reduction through clustering, sequence count table generation, and the calculation of sequence properties such as GC content and tetramer frequencies. A predefined set of host genomes is available for this step, with the option to incorporate custom genomes as needed. Taxonomic classification is performed through an iterative search strategy using MMSeqs2. Initially, query sequences from the preprocessing step are aligned against a virus-specific protein database in a translated nucleotide-to-protein search. Sequences assigned a viral taxonomy at this stage undergo a secondary validation against a broader reference database encompassing bacteria, plants, vertebrates, fungi, and other domains of life. This step is necessary to eliminate false positives arising from the restricted nature of the initial viral protein database. Although directly performing the secondary search would be more comprehensive, it is computationally prohibitive due to the vast size of general sequence databases. Thus, the two-step approach efficiently captures potential viral sequences while refining classification accuracy. For population-level analysis, assembly is performed to generate contigs, which serve as a foundation for subsequent investigations. This process employs a resource-efficient strategy designed to maximize species representation. First, individual assemblies are generated using MEGAHIT or Canu for each sample. Reads are mapped back to these assemblies, and any unmapped reads undergo an additional round of assembly. The resulting contigs are merged into a non-redundant dataset using Flye. These contigs are then annotated with MMSeqs, while a complementary pseudo-consensus annotation is performed by integrating read mapping data with taxonomic assignments from the sequence table. This approach enhances the resolution of contig origins and facilitates downstream virome analysis. Sample preparation for metabolome analysis After thawing the sample slowly at 4 °C, weigh 25 mg and place it into a 1.5 mL Eppendorf tube. Add 800 μL of extraction solution (methanol: acetonitrile: water = 2:2:1, v:v:v, pre-cooled to -20 °C) and 10 μL of internal standard. Add two small steel balls and grind the sample using a tissue grinder (50 Hz, 5 min). Perform ultrasound treatment in a 4 °C water bath for 10 min. Incubate the sample in a refrigerator at 20 °C for 1 hour. Centrifuge at 25,000 g for 15 min at 4 °C. After centrifugation, transfer 600 μL of the supernatant into a freeze vacuum concentrator to dry. Then, re-dissolve the residue in 600 μL of complex solution (methanol: H₂O = 1:9, v:v). Vortex for 1 min, perform ultrasound treatment in a 4 °C water bath for 10 min, and centrifuge at 25,000 g for 15 min at 4 °C. Transfer the supernatant into the sample vial. For quality control, mix 50 μL of supernatant from each sample to create QC samples, which are used to evaluate the repeatability and stability of the LC-MS analysis process. The sample extracts were analyzed using a Waters UPLC I-Class Plus (Waters, USA) equipped with a QTRAP 6500 Plus mass spectrometer (SCIEX, USA). After importing the offline mass spectrometry data into Compound Discoverer 3.3 (Thermo Fisher Scientific, USA) and analyzing it using the BMDB (BGI Metabolome Database), mzCloud database, and ChemSpider online database, a data matrix containing information such as metabolite peak areas and identification results was generated. The table was then further analyzed and processed. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) Total RNA of the kidney, trachea, and ileum at 3, 5 and 7 dpi were extracted by TRIzol Reagent (Invitrogen Life Technologies) and reverse transcribed using a ReverTra Ace® qPCR RT Master Mix with gDNA Remover kit (TOYOBO, Osaka, Japan). Relative quantitative PCR was performed for the detection of the N gene [33] using Hieff™ qPCR SYBR® Green Master Mix (Yeasen, Shanghai, China) on a Light-Cycler 480 PCR system (Roche, Basel, Switzerland), and the GAPDH gene was used as the reference gene for normalization. The cycling conditions were as follows: 95 ℃ for 5 min, followed by 40 cycles of 95 °C for 10 s, 58 °C for 10 s, and 72 °C for 30 s, with subsequent incubations at 95 °C for 5 s, 60 ℃ for 1 min and 95 °C for 15 s. Data were analyzed using the 2 −ΔΔCT method and presented as the change (n-fold) relative to either the control group inoculated with PBS or non-infected cells. Western blot Collected chicken kidney tissues were denaturated and lysed after different hours post infection. The samples were fractionated by electrophoresis on 12 % SDS-PAGE gels, and resolved proteins were transferred onto polyvinylidene difluoride (PVDF) membranes. After blocking with 5% Bovine Serum Albumin (BSA), the membranes were incubated with anti IBV-N mouse monoclonal antibody (MyBioSource) in 1 % BSA in TBST for 1 h, followed by HRP-conjugated goat anti-mouse IgG(H+L) secondary antibody (Proteintech). Histopathology and immunohistochemistry The trachea and kidney tissues were fixed, dehydrated, transparentized, and embedded in paraffin. After fixation and decalcification, the tissues were sectioned to 5 μm. H&E staining, and immunohistochemistry were performed separately according to the instructions of the reagent. The section was captured using a Nikon TS2 microscope (Nikon Instech, Tokyo, Japan). Phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt2) For functional profiling, we employed Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUSt2) [34], a bioinformatics tool that predicts microbial gene functions based on 16S rRNA gene sequencing data. This tool infers potential metabolic pathways encoded by the microbiota by leveraging reference genome databases. The functional annotations of bacterial gene functions were performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Differential abundance analysis of both bacterial taxa and predicted gene functions was conducted using STAMP, with statistical significance determined at p < 0.05. Enzyme-linked immunosorbent assay (ELISA) for cytokine quantification Serum levels of inflammatory cytokines were quantified using commercial ELISA kits, following the manufacturers’ protocols. Absorbance measurements were recorded at 450 nm using a microplate reader (Bio-Rad, USA). Specifically, IL-16, IFN-α, IFN-γ, IL-10, IL-21, IL-2, IL-6, and CCL4 were analyzed using the Millipore MILLIPLEX Chicken Cytokine/Chemokine Panel 1 ELISA kit (GCYT1-16K-PX12). Additionally, IL-1β (CSB-E11230Ch), IFN-β (CSB-E11237Ch), and TNF-α (CSB-E11231Ch) levels were determined using separate ELISA kits. Mantel test for correlation analysis of microbiome, virome, and metabolome To evaluate correlations among microbiome, virome, and metabolome datasets, a Mantel test was performed, and the relationships were visualized using a correlation heatmap. The microbiome, virome, and metabolome datasets were imported into R, where Pearson correlation coefficients were computed for environmental factors. Statistically significant correlations were extracted and stored for further analysis. The Mantel test was applied to assess inter-dataset associations, with results categorized and visualized. A correlation heatmap was generated, highlighting statistically significant Pearson correlation values. Additionally, Mantel test results were overlaid on the heatmap using connecting lines of varying thickness and color to represent correlation strength and significance. Prediction of bacterial phage host range using integrated phage host prediction (iPHoP) iPHoP (Integrated Phage HOst Prediction) was employed to predict the host range of bacterial phages [35]. All assembled phage contigs were selected and imported into iPHoP in FASTA format. The analysis was conducted using default parameters, leveraging BLAST, CRISPR, and iPHoP-RF methodologies for host prediction. The resulting host predictions were subsequently imported into R for downstream visualization. Construction and visualization of the microbiome-virome interaction network To construct and visualize microbiome-virome interactions, raw microbiome and virome abundance data were uploaded to http://cloudtutu.com.cn/. Pearson’s correlation analysis was conducted to assess relationships between microbiomic and viromic datasets, applying a filtering criterion of |r| ≥ 0.5 and p ≤ 0.05. The resulting correlation data were exported and imported into R for network visualization. A microbiome-virome interaction network was constructed using a force-directed layout algorithm. The network graph was plotted with annotations indicating modular structures, node classifications, edge weights, and node abundances to enhance interpretability. Results Age-dependent susceptibility to IBV highlighted by clinical and experimental insights Based on a decade-long epidemiological survey conducted by our research team, IBV exhibits a significantly higher mortality rate in younger chickens compared to older ones. Among adult chickens, the mortality rate caused by IBV was observed to be only 4.8%. In contrast, the mortality rate in young chickens reached 29.8% (Fig. 1A). The epidemic IBV strains were isolated, propagated, and genotyped, revealing that QX-like IBV is the most prevalent field strain, accounting for 53.1% of isolates (Table S1). To validate these clinical observations, we established an IBV infection model using the predominant QX-like genotype strain to infect young (10–20 days old) and adult (60–70 days old) yellow-feathered broilers. The results revealed that IBV caused a mortality rate of 10% in adult chickens but 60% in adult chickens (Fig. 1B). RT-qPCR analysis of viral loads in the kidney, trachea, and ileum on 3, 5, and 7 days post infection (dpi) demonstrated significantly higher viral loads in the kidneys of young chickens compared to adult chickens on 5 and 7 dpi (Fig. 1C). However, no significant differences were observed in the viral loads of the trachea and ileum (Fig. S1A,B). On the 5 dpi, clinical autopsy showed no apparent symptoms in adult chickens, whereas young chickens exhibited hallmark symptoms, including tracheal hemorrhage and mottled kidneys (Fig. S1C). Histopathological analysis revealed severe hemorrhage and inflammatory cell infiltration in the kidneys of young chicken post-infection (Fig. S1D), immunohistochemistry (IHC) analysis demonstrated higher viral loads of kidney and trachea in young chickens than that in adult chickens (Fig. S1E). Collectively, these findings corroborate our clinical observations that IBV causes more severe clinical symptoms and higher mortality rates in younger chickens compared to adults. To investigate whether the differences in IBV-induced mortality rates between young and adult chickens could be attributed to gut microbiota, we developed a gut microbiota transplantation model (Fig. 1D). Fecal microbiota from adult or young chickens were transplanted into 6-day-old recipient specific pathogen free (SPF) young chickens pre-treated with antibiotics for five days. The recipient young chickens were subsequently challenged with QX genotype IBV at 10 days of age and monitored for 15 days post-infection (Fig. 1D). The results demonstrated that transplantation of adult chicken fecal microbiota significantly improved the survival rates of recipient young chickens following IBV infection, whereas transplantation of young chicken fecal microbiota had no significant impact on recipient survival (Fig. 1E). Furthermore, the young chickens that receiving adult chicken fecal microbiota exhibited significantly lower viral loads in their trachea, kidney, and ileum compared to those receiving young chicken fecal microbiota (Fig. 1F,G and Fig. S2A,D), and the pathological damage induced by IBV in both the kidney and trachea has also been markedly reduced (Fig. S2B,C), and the growth performance was also restored (Fig. 1H). These findings confirm that gut microbiota can influence IBV resistance in young chickens. Gut microbial profiling reveals divergent community structures across adult, young, and young-FMT chickens The gut microenvironment is a complex ecosystem comprising diverse bacteria, viruses, and metabolites that collectively regulate host immunity and health [36]. To elucidate the mechanisms underlying the differential effects of the gut microbiota from adult chickens on IBV resistance in young chickens, we employed an integrative approach combining microbiomics, viromics, and untargeted metabolomics to characterize the gut microenvironment across these groups. 16S rRNA gene sequencing was used to detect the chicken gut microbiota. Partial least squares discrimination analysis (PLS-DA) of microbiomic sequencing data demonstrated robust group clustering and significant divergence in gut microbial composition among adult chickens, young chickens, and young chickens treated with adult chicken fecal microbiota transplantation (young-FMT) (Fig. 2A). Among the 1,662 operational taxonomic units (OTUs) identified, 804 were shared across the three groups, whereas 1,390 OTUs (83.6%) were shared between adult and young-FMT chickens, suggesting efficient colonization of adult chicken microbiota in young chicken guts following FMT (Fig. 2B and Fig. S3). Alpha diversity analysis further revealed that the gut microbiota of adult and young-FMT chickens exhibited significantly greater diversity compared to young chickens (Fig. 2C,D and Fig. S4A,B). Taxonomic classification at the phylum, class, order, family, genus, and species levels revealed substantial differences in microbial composition across three groups (Table S2), the gut microbiota of young-FMT chickens closely resembled that of adult chickens at nearly all taxonomic levels (Fig. 2E–H and Fig. S4C–J). At the class level, young chickens exhibited a predominance of Clostridia (76.8%), which was considerably higher than in adult (42.8%) and young-FMT chickens (64.1%). In contrast, adult and young-FMT chickens showed significantly higher abundances of Synergistia (22.0% and 17.2% vs. <0.1%), Deltaproteobacteria (4.5% and 0.9% vs. 0.2%), and Coriobacteriia (1.8% and 1.0% vs. 0.5%)(Fig. 2E). Linear discriminant analysis effect size (LEfSe) analysis highlighted top 10 taxa enriched in adult chickens versus to at the genus level, including Cloacibacillus , Desulfovibrio , Megasphaera , Akkermansia , and Thermophilibacter (Fig. 2I,J). The top 10 genus in young-FMT chickens versus to young chickens including Cloacibacillus , Akkermansia , and Mediterranea (Fig. S5A,B). In contrast, the microbiota differences between adult and young-FMT chickens were minimal (Fig. S5C,D). Functional predictions using PICRUSt2 further revealed distinct microbial metabolic pathway enrichments between adult and young chickens. The gut microbiota of adult chickens was particularly enriched in pathways associated with energy metabolism and digestive system functions (Fig. S6 and Table S3). Collectively, these findings provide a comprehensive framework for understanding the unique gut microbial compositions and functional capacities of adult, young, and young-FMT chickens, emphasizing their roles in differential IBV resistance. Metatranscriptomic insights into gut virome dynamics in adult, young, and young-FMT chickens The gut virome, a critical component of the intestinal microenvironment, was analyzed in this study using metatranscriptomic approach. PLS-DA results revealed high intra-group reproducibility and distinct clustering of virome structures among adult chickens, young chickens, and young-FMT chickens (Fig. 3A). Alpha diversity analysis further demonstrated that adult and young-FMT both harbored a richer virome compared to young chickens, while less significant differences were observed between adult and young-FMT chickens (Fig. 3B–E). Taxonomic annotation of sequencing reads revealed substantial differences in viral family abundances. At the phylum level, Parvoviridae , Smacovirida e, Circoviridae , Microviridae , and Picobirnaviridae were more prevalent in adult and young-FMT chickens, whereas Sedoreoviridae , Ackermannviridae , Coronaviridae , and Picornaviridae dominated in young chickens (Fig. 3F). At the genus level, adult and young-FMT chickens showed higher abundances of Picobirnavirus , Tremovirus , Avisivirus , Posavirus , and Microvirus , while Gammacoronavirus , Gallivirus , Rotavirus , and Megrivirus were more abundant in young chickens (Fig. 3G). These differences extended across taxonomic ranks, highlighting consistent divergence in the gut virome composition between different ages of chickens (Fig. 3F,G; Fig. S7A,B and Table S4). Notably, the gut virome profiles of adult and young-FMT chickens displayed similar patterns, indicating effective virome colonization from adult chicken feces to the recipient young chickens following FMT. After filtering low-quality contigs, assembly of viral reads yielded 1,083 high-quality viral contigs, ranging from 1,001 to 27,574 nucleotides in length, belonging to families such as Picornaviridae , Coronaviridae , and Astroviridae (Fig. S8A). Phylogenetic analysis comparing these contigs with their respective reference genomes revealed significant genetic divergence, suggesting evolutionary adaptations within the gut virome (Fig. S8B–D). Assembled DNA viruses were predominantly bacteriophages, which play a potential regulatory role in the gut microbial ecosystem. A total of 51 phage contigs were subjected to host prediction, including members of the families Microviridae , Herelleviridae , and Ackermannviridae . Using the iPHoP software, which integrates BLAST, CRISPR, and iPHoP-RF algorithms, host prediction identified 13 phage contigs from Microviridae family targeting the genus Faecalibacterium and 7 phage contigs Herelleviridae family from targeting the genus Stercorousia (Fig. S9A–C and Table S5). Moreover, complex interactions between bacteria and viruses within the gut microenvironment were explored, as these interactions are known to regulate host health and disease outcomes [37]. Interplay networks encompassing bacteria-virus, bacteria-bacteria, and virus-virus interactions were constructed for adult and young chickens, respectively (Fig. S10, Table S6). In adult chickens, 2,581 interaction pairs were identified, while in young chickens, the number increased to 5,551. These findings provide a foundational understanding of the intricate avian gut microenvironment and its potential regulatory mechanisms. Metabolomic characterization uncovers distinct gut metabolic pathways in adult, young, and young-FMT chickens Gut cells and microbiota produce a diverse array of metabolites that play essential physiological roles, shaping a complex and dynamic gut microenvironment in coordination with bacteria and viruses. Untargeted metabolomic analysis identified a total of 3,115 gut metabolites in chickens, comprising 523 lipids (16.8%) and 414 amino acids, peptides, and analogs (13.3%) (Fig. S11A and Table S7). The primary functions of metabolites include amino acid metabolism, biosynthesis of other secondary metabolites, and lipid metabolism (Fig. S11B). Partial least squares discrimination analysis (PLS-DA) revealed high intra-group consistency and significant differences in gut metabolite profiles among adult, young, and young-FMT chickens, with adult and young-FMT chickens clustering more closely together than young chickens. (Fig. 4A). A total of 2,239 differentially expressed metabolites were identified in adult chickens compared to young chickens, with 1,226 metabolites upregulated and 1,013 downregulated. Additionally, the differentially expressed metabolites between young-FMT and young chickens, as well as adult and young-FMT chickens, were also identified. (Fig. 4B–D and Table S8). And the metabolic patterns of adult chickens gut significantly differ to that of young chickens, but exhibit a more similar trend compared to that of young-FMT chickens (Fig. 4E,F). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that these differentially expressed metabolites between adult and young were enriched in pathways related to neuroactive ligand-receptor interaction, ABC transporters and tryptophan metabolism (Fig. 4G and Table S9). The expression level of differentially expressed metabolites in the most significant KEGG pathway, neuroactive ligand-receptor interaction, revealed similar pattern between adult and young-FMT groups, which is different from young group (Fig. 4H). Cross-referencing enriched pathways from metabolomic data with those predicted by microbiome analyses identified 40 shared pathways, highlighting strong concordance between the two datasets (Fig. S11C). Correlation analysis using Mantel tests revealed statistically significant associations between gut metabolites and microbial families, such as Campylobacteraceae (Mantel’s r > 0.5 and Mantel’s p 0.25 and Mantel’s p < 0.01) (Fig. S11D). Dynamic modulation of gut microbiota in a low-dose IBV infection model High-dose infectious bronchitis virus (IBV) challenge models frequently result in substantial mortality among young chickens, hindering the ability to study longitudinal changes in gut microbiota. To overcome this limitation, a low-dose infection model was developed to track gut microbiota dynamics over time without inducing fatality. Fecal samples were collected pre-infection (0) and at 3, 5, and 7 days post-infection for 16S rRNA gene sequencing (Fig. 5A). PLS-DA analysis revealed consistent intra-group clustering and significant shifts in gut microbial composition following IBV infection (Fig. 5B). Across all samples, 971 operational taxonomic units (OTUs) were shared between pre- and post-infection groups, reflecting core microbiota retention despite infection (Fig. 5C and Table S10). Alpha diversity analysis indicated a transient decline in microbial richness on days 3 and 5 post-infection, followed by partial recovery by day 7 (Fig. S12A–D). At the species level, bacterial abundance exhibited dynamic fluctuations during infection. For instance, Phocaeicola caecigallinarum showed an initial decline followed by recovery, whereas Akkermansia muciniphila ( A. muciniphila ) displayed a continuous upward trend (Fig. 5D). Among the top 10 most abundant taxa, A. muciniphila and Kineothrix alysoides demonstrated significant increases on days 7 and 3 post-infection, respectively (Fig. 5E and Fig. S12E). Notably, A. muciniphila showed the highest fold-change, with abundance rising 6.3-fold and 6.6-fold on days 5 and 7, respectively (Fig. 5F). LEfSe analysis identified distinct microbial signatures between pre-infection and day 7 post-infection samples, with Akkermansia emerging as the most enriched genus on day 7 (Fig. 5G,H). This finding aligns with previous observations that A. muciniphila is a key biomarker in the gut microbiota of adult and young-FMT chickens, where it is significantly more abundant than that in young chickens (Fig. 2H–J and Fig. S4D,J). Additionally, two other species, Cloacibacillus porcorum ( C. porcorum ) and Neglecta timonensis ( N. timonensis ), were upregulated following IBV infection and exhibited higher abundances in adult and young-FMT chickens compared to young chickens (Fig. S13A–C). Protective role of A. muciniphila in enhancing young chickens’s resistance to IBV infection The significant upregulation of A. muciniphila , C. porcorum , and N. timonensis following IBV infection, alongside their higher abundance in the gut microbiota of adult and young-FMT chickens, prompted the hypothesis that these bacteria contribute to resistance of chickens against IBV. To test this, the three bacterial strains were procured, propagated, and administered individually to young chickens by oral gavage prior to IBV challenge (Fig. 6A). Among the treatments, A. muciniphila markedly improved survival rates of chickens to 90%, compared to 65% for C. porcorum and 50% for N. timonensis (Fig. 6B,C). These results establish A. muciniphila as a key factor in conferring resistance to IBV in chickens, with C. porcorum and N. timonensis providing moderate, yet limited, protective effects. Given the pronounced efficacy of A. muciniphila , subsequent investigations focused on this bacterium. Oral administration of A. muciniphila significantly reduced pathological damage and viral loads in the kidneys and trachea of infected young chickens by day 5 post-infection (Fig. 6D–G and Fig. S14). Serum analysis on 5 dpi revealed reduced levels of pro-inflammatory cytokines (IL-1β, IL-6, and IFN-γ) and elevated levels of antiviral interferons (IFN-α and IFN-β) in A. muciniphila -treated chickens (Fig. 6F). This immune modulation highlights the A. muciniphila ’s role in reducing inflammation and enhancing antiviral responses. To further elucidate the underlying mechanism, metabolomic profiling of gut samples collected on day 5 post-infection was conducted. The analysis identified 53 upregulated and 58 downregulated metabolites in A. muciniphila -treated and IBV-infected group compared to the IBV-infected group (Fig. 6I,J and Fig. S15A), the KEGG function of differentially expressed metabolites were also enriched (Fig. S15B). Among these, γ-aminobutyric acid (GABA) exhibited the highest fold increase (Fig. 6K). Consistent with prior findings, GABA levels were also significantly higher in the gut of adult chickens compared to young chickens (Fig. S14A–B). GABA, a four-carbon non-proteinogenic amino acid, is known for its critical roles in various physiological and biochemical processes across organisms [38]. Notably, GABA has been reported to regulate immune responses, including the inhibition of IL-1β production[39]. These observations suggest that A. muciniphila enhances IBV resistance in young chickens by modulating GABA levels, which subsequently suppress IL-1β production and mitigate inflammation. GABA modulates NF-κB signaling to enhance chick survival following IBV infection To confirm the role of GABA in enhancing IBV resistance, we administered GABA to young chickens prior to IBV challenge and monitored their survival (Fig. 7A). GABA treatment significantly improved chick survival rates following IBV infection (Fig. 7B). Serum cytokine analysis on day 5 post-infection revealed that GABA administration markedly suppressed pro-inflammatory cytokines (IL-1β, IL-6, TNF-α, and IFN-γ) while promoting antiviral interferons (IFN-α and IFN-β) (Fig. 7C–E and Fig. S17A,B). GABA also mitigated the pathological damage induced in kidney and trachea, and reduced the viral loads in these tissues (Fig. S17C,D). IBV infection induces inflammation and tissue damage via activation of the NF-κB signaling pathway [40]. GABA has been shown to inhibit inflammation by reducing IL-1β expression [39]. To investigate whether the anti-inflammatory effects of GABA are mediated through NF-κB signaling, we examined p65 protein expression in the kidneys of infected young chickens on 5 dpi. GABA treatment significantly suppressed IBV-induced p65 phosphorylation and nuclear translocation, thereby mitigating NF-κB-mediated inflammatory responses and kidney tissue damage (Fig. 7G–K). These findings confirm that GABA plays a pivotal role in regulating immune responses and inflammation during IBV infection, primarily by modulating the NF-κB signaling pathway. Discussion A decade of clinical observations reveal that adult chickens exhibit stronger resistance to IBV than young chickens, but the role of the gut microbiome remain unclear. Given its crucial role in poultry development and health [41, 42] and the industry’s shift toward antibiotic-free farming [43, 44], understanding the role of gut microbiome in IBV resistance is of both scientific and practical significance. In this study, we demonstrate that FMT from adult chickens significantly enhances IBV resistance in young chickens. Using an integrated multi-omics approach, we systematically characterize the gut microbiomic, viromic, and metabolomic profiles of adult, young, and young-FMT chickens, showing that FMT-treated young chickens developed a microbiota composition closely resembling that of adults. Further investigations using a novel persistent IBV infection model identifies A. muciniphila , C. porcorum , and N. timonensis as key probiotic candidates, with A. muciniphila exhibiting the strongest protective effect. Mechanistic study reveals that A. muciniphila enhances IBV resistance by increasing GABA production, which suppresses NF-κB-driven inflammation and upregulates antiviral interferon expression. These findings establish A. muciniphila as a promising probiotic for IBV control and highlight microbiota modulation as a viable strategy for enhancing antiviral immunity in poultry. IBV is one of the most significant viral pathogens affecting the global poultry industry, causing millions of dollars in economic losses due to its high prevalence, broad tissue tropism, and severe morbidity and mortality rates [21]. IBV is one of the most significant viral pathogens affecting the global poultry industry, causing millions of dollars in economic losses due to its high prevalence, broad tissue tropism, and severe morbidity and mortality rates [45]. However, their efficacy is limited by inadequate cross-protection against diverse IBV variants [45, 46]. Moreover, the absence of approved antiviral drugs, coupled with restrictions on drug use in poultry industry, underscores the need for alternative strategies that align with antibiotic-free and sustainable farming policies. While previous studies have demonstrated the critical role of gut microbiota in IBV resistance [47], its potential as a therapeutic intervention remains unclear. Here, we confirm that FMT from adult chickens significantly improves the survival rate of young chickens following IBV infection (Fig. 1D, E), a crucial finding given that IBV causes the most severe damage in young birds [45]. Additionally, FMT reduces viral loads (Fig. 1F,G and Fig. S2D) and mitigates tissue damage in the kidneys and trachea (Fig. S2B,C), the primary target organs of IBV [48]. FMT has been demonstrated as a promising strategy for significantly enhancing antioxidant capacity, immune function, and intestinal glucose transport in young chickens [41].Our findings highlight FMT as a promising microbiome-based therapeutic strategy for IBV control and a sustainable alternative for poultry disease management in the context of antibiotic-free farming. By integrating multi-omics analyses, we comprehensively characterized the gut microbiome of adult chickens, young chickens, and young chickens treated with FMT. Compared to young chickens, young-FMT chickens exhibited a significant decrease in Bacillota and an increase in Synergistota , mirroring the microbial composition of adult donor chickens (Fig. S4C). This shift indicates the successful colonization of transplanted microbiota. Previous studies have similarly reported that FMT reshapes the gut microbiome of recipient chickens, aligning it closely with that of donors [49]. However, while FMT transfers not only bacteria but also viruses and metabolites, little research has explored these additional components. In this study, we further examined viromic and metabolic profiles post-FMT and found that young-FMT chickens exhibited viral (Fig. 3F,G) and metabolic (Fig. 4F) compositions similar to those of adult donors, distinguishing them from untreated young chickens. While prior research has shown that FMT alters the metabolome of recipients, it has not directly compared these changes with donor metabolic profiles [50, 51]. Our results suggest that FMT facilitates not only beneficial bacterial colonization but also the transfer of advantageous metabolites, such as GABA (Fig. 4H). However, we also observed an increased relative abundance of IBV in young-FMT chickens compared to untreated young chickens, potentially due to viral transmission from adult donors (Fig. 3G). This concern aligns with warnings from the U.S. Food and Drug Administration (FDA) regarding the risk of transmitting SARS-CoV-2 during FMT procedures [52]. Therefore, to ensure the safety of FMT-based interventions in poultry, stringent screening of FMT donors for pathogenic viruses is essential to mitigate potential transmission risks. In a newly established persistent IBV infection model, we identified three candidate probiotics, A. muciniphila , C. porcorum , and N. timonensis , and validated their roles in enhancing IBV resistance in chickens. Notably, administration of A. muciniphila significantly improved chick survival to 90–95%, whereas other two bacterium exhibited relatively modest effects (Fig. 6B–C). Among microbial species, A. muciniphila emerged as the most promising, given its well-documented benefits in gut health, aging mitigation, and metabolic regulation [53-58]. Recent studies have shown its antiviral potential against Bunyaviridae in mice [59], and our findings extend its therapeutic scope to gamma-coronavirus infections, demonstrating its potential for improving poultry disease resistance. Given the growing resistance to traditional antiviral drugs, probiotic-based strategies present a viable alternative, offering safety, cost-effectiveness, and immunomodulatory advantages [60, 61]. Our study lays the groundwork for leveraging A. muciniphila and other beneficial bacteria in poultry farming and highlights the potential of multi-strain probiotic formulations to enhance therapeutic efficacy [62]. IBV infection induces severe inflammation in the kidneys and trachea, leading to high mortality in chickens [63]. Excessive inflammation is also a hallmark of coronavirus infections, including SARS-CoV-2, where cytokine storms drive severe outcomes [64]. Controlling inflammation is therefore a key strategy in mitigating disease severity. The gut microbiota plays a crucial role in regulating host inflammation and immunity via direct and indirect mechanisms [65-68]. For instance, extracellular vesicles from Roseburia intestinalis suppress inflammation and improve gut health [66]. Similarly, A. muciniphila modulates host immunity through extracellular vesicles and other pathways [69, 70]. Previous studies have shown that its membrane phosphatidylethanolamine (PE) influences cytokine secretion and dendritic cell activation, fine-tuning immune responses [71]. Our study demonstrated that A. muciniphila enhances IBV resistance by increasing GABA levels, which in turn suppress pro-inflammatory cytokines and alleviate kidney inflammation (Fig. 7G). GABA, a widely distributed non-proteinogenic amino acid, functions as a neurotransmitter and modulator of immune and metabolic processes [39, 72, 73]. Mechanistically, GABA inhibits IL-1β expression in macrophages by modulating NF-κB signaling and inflammasomes [39]. IBV-infected young chickens, NF-κB-mediated inflammation was excessively activated, but A. muciniphila -induced GABA effectively suppressed this response. This study leveraged multi-omics approaches, integrating microbiomics and metabolomics, to explain the observed differential IBV resistance between young chickens and adult chickens. We identified A. muciniphila as a key functional member of the gut microbiota responsible for IBV resistance. Mechanistically, we demonstrated that A. muciniphila enhances resistance by upregulating GABA, thereby suppressing NF-κB-driven inflammation, mitigating nephritis, and strengthening antiviral immunity. These findings offer novel insights into microbiota-mediated antiviral defense and establish a scientific framework for advancing microbiome-targeted live biotherapeutic products (LBPs) as interventions against IBV and other emergent viral pathogens. Nevertheless, this study has certain limitations. First, our findings are based on yellow-feathered broilers, and further validation in other commercially important chicken lines, such as white-feathered broilers, is needed. Second, IBV is highly diverse, with at least eight circulating genotypes. Our study focused on the predominant QX-like genotype, and whether A. muciniphila confers protection against other variants remains to be determined. Additionally, future studies should assess whether combining A. muciniphila with C. porcorum , N. timonensis , or a multi-strain probiotic formulation could enhance IBV resistance. Addressing these gaps will help refine probiotic applications across different poultry populations and viral strains. Conclusion Our study provides novel insights into how A. muciniphila confers IBV resistance via GABA-mediated immunomodulation, offering a new paradigm for microbiota-driven antiviral strategies in poultry. These findings pave the way for microbiome-based interventions. Future research should focus on refining probiotic applications, optimizing multi-strain formulations, and further elucidating microbiota-metabolite interactions in viral disease resistance. As antibiotic-free farming continues to gain traction, FMT-based biological control strategies hold immense promise for sustainable poultry health management and disease prevention. Declarations Authors’ contributions YC, HZ and LD : conceived and designed the research. OP, YL, HZ and YC: designed experiments. OP, YL, XW, YH2, YY, QK, RG, GH, AL, FH, YX, XL, JL, YH2, ZK, YD and YZ: performed lab and animal experiments. CX, ZH and WL: provided provided resources. OP, YL, XW and LD: performed the data visualization. OP, XW, YH1 and LD: performed bioinformatics analysis. YC: provided funding support. OP, YL, LD and HZ: interpreted data. OP, YL, LD, HZ and YC: wrote and revised the manuscript. All authors provided insights, read and approved the final manuscript. Acknowledgments We are grateful to the State Key Laboratory of Biocontrol (Sun Yat-sen University) for supporting this work. This study was supported by National Key Research and Development Program, China (2021YFD1801101). We also sincerely thank Dr. Qingfeng Zhou, Dr. Lijuan Yin, Dr. Zhuanqiang Yan, and other colleagues in Research Institute of Wen’s Food Group for helping us to collect clinical samples and conduct the animal experiments. Competing interests The authors declare no competing interests. Ethics approval and consent to participate All sample collection and animal experiments in this study were conducted in accordance with the guidelines of the Animal Ethics Committees of Sun Yat-sen University and Harbin Veterinary Research Institute (HVRI) (2101112-01 and 230721-02-GR). Data availability The raw sequencing data generated in this study, including 16S rRNA gene sequencing of 100 samples and metatranscriptomic sequencing of 60 samples, have been deposited in the National Center for Biotechnology Information (NCBI) database under the accession numbers PRJNA1219767 (https://dataview.ncbi.nlm.nih.gov/object/PRJNA1219767?reviewer=s96ihp5f67me7bt60gacd2ig6i) and PRJNA1222581 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1222581). The untargeted metabolomics data of 100 samples have been deposited in the MetaboLights database at the European Molecular Biology Laboratory (EMBL)–European Bioinformatics Institute (EBI) under the accession number MTBLS12310 (https://www.ebi.ac.uk/metabolights/reviewer694c2e26-ed7a-464f-a12d-c0a70f0ded83). References Delzenne, Nathalie M., Laure B. Bindels, Audrey M. Neyrinck, Jens Walter. 2025. “The gut microbiome and dietary fibres: implications in obesity, cardiometabolic diseases and cancer.” Nature Reviews Microbiology 23: 225-238. https://doi.org/10.1038/s41579-024-01108-z de Vos, Willem M., Herbert Tilg, Matthias Van Hul, Patrice D. Cani. 2022. “Gut microbiome and health: mechanistic insights.” Gut 71: 1020-1032. https://doi.org/10.1136/gutjnl-2021-326789 Wu, Guojun, Ting Xu, Naisi Zhao, Yan Y. 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Supplementary Files TableS1.xlsx TableS3.xlsx TableS5.xlsx TableS9.xlsx TableS6.xlsx TableS2.xlsx TableS2.xlsx TableS8.xlsx TableS4.xlsx TableS11.xlsx TableS10.xlsx TableS7.xlsx SupplementaryMaterials.docx Cite Share Download PDF Status: Posted Version 1 posted 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-6786224","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475944053,"identity":"61aafdeb-1746-45a7-9a50-445c9a949285","order_by":0,"name":"Ouyang Peng","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Ouyang","middleName":"","lastName":"Peng","suffix":""},{"id":475944054,"identity":"802413f4-0543-4944-b43d-8143359cb807","order_by":1,"name":"Yufang Liu","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yufang","middleName":"","lastName":"Liu","suffix":""},{"id":475944055,"identity":"25f58b89-6d59-45eb-ad6d-cbd6cad05aef","order_by":2,"name":"Xuanci Wang","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Xuanci","middleName":"","lastName":"Wang","suffix":""},{"id":475944056,"identity":"80b6a329-3e66-46a9-aaa9-f651ee18890a","order_by":3,"name":"Yihui Huang","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yihui","middleName":"","lastName":"Huang","suffix":""},{"id":475944057,"identity":"9ef8e71a-9fd0-40d5-aa98-0ea7419cb657","order_by":4,"name":"Yiqin Yang","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yiqin","middleName":"","lastName":"Yang","suffix":""},{"id":475944058,"identity":"70dc8cb4-58b7-4d46-acff-20ade5f12378","order_by":5,"name":"Qixiang Kang","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Qixiang","middleName":"","lastName":"Kang","suffix":""},{"id":475944059,"identity":"6a417b74-0385-4553-b94e-30d285bfe77d","order_by":6,"name":"Rui Geng","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Geng","suffix":""},{"id":475944060,"identity":"724df42f-c4a1-4d72-962e-2d3bf8c7d2ca","order_by":7,"name":"Guangli Hu","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Guangli","middleName":"","lastName":"Hu","suffix":""},{"id":475944061,"identity":"f4b0a228-4624-48de-83fa-9ce352a941e6","order_by":8,"name":"Aiping Lv","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Aiping","middleName":"","lastName":"Lv","suffix":""},{"id":475944062,"identity":"1e86ce02-5c9a-4285-948d-ef29ec7747cc","order_by":9,"name":"Fangyu Hu","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Fangyu","middleName":"","lastName":"Hu","suffix":""},{"id":475944063,"identity":"2cdf5cf1-2b90-4fdc-8be4-4f78987fae06","order_by":10,"name":"Yongbo Xia","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yongbo","middleName":"","lastName":"Xia","suffix":""},{"id":475944064,"identity":"577a0773-059c-4994-b199-be6ac839b83d","order_by":11,"name":"Xin Luo","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Luo","suffix":""},{"id":475944065,"identity":"7bd25386-fd70-4adf-b412-ceac1dcb65c7","order_by":12,"name":"Jiamin Liao","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Jiamin","middleName":"","lastName":"Liao","suffix":""},{"id":475944066,"identity":"8ceefd9e-4bd3-4098-8781-63b24068fdb6","order_by":13,"name":"Yihong He","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yihong","middleName":"","lastName":"He","suffix":""},{"id":475944067,"identity":"ac9399a9-9a0b-4e64-8d7c-d1b3849defaf","order_by":14,"name":"Zhanpeng Kuang","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Zhanpeng","middleName":"","lastName":"Kuang","suffix":""},{"id":475944068,"identity":"3d63925b-5892-42a7-a800-3375fbd7193f","order_by":15,"name":"Yunping Du","email":"","orcid":"","institution":"Wen’s Foodstuff Group Co. 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The Kaplan-Meier (K-M) analysis was used to analyze the significance between groups in graph (B) and (E) (**, *** or **** indicate \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 and \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.0001, respectively; ns indicates not significant). Bar graphs in (C), (F), and (G) represent the mean ± SEM, with error bars indicating the SEM (* or ** indicate \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 and \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, respectively; ns indicates not significant). The Student’s t test was used for each comparison.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6786224/v1/ff1d3bb3e1edb448168c2654.jpg"},{"id":85385409,"identity":"a9d384aa-2538-40c5-8237-d106b0db3bef","added_by":"auto","created_at":"2025-06-25 09:45:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":335807,"visible":true,"origin":"","legend":"\u003cp\u003e16S rRNA gene sequencing analysis of gut microbiota in adult chickens, young chickens, and FMT-treated young chickens. \u003cstrong\u003eA\u003c/strong\u003e Partial least squares discriminant analysis (PLS-DA) showing differences in gut microbiota composition among the groups (\u003cem\u003en\u003c/em\u003e = 20). \u003cstrong\u003eB\u003c/strong\u003e Venn diagram illustrating shared and unique operational taxonomic units (OTUs) among the groups. \u003cstrong\u003eC–D\u003c/strong\u003e Comparison of alpha diversity indices: Chao1 (C) and Sobs (D) among the groups. \u003cstrong\u003eE–H\u003c/strong\u003e. Gut microbiota composition at the class (E), order (F), family (G), and genus (H) levels across the groups. \u003cstrong\u003eI\u003c/strong\u003e LEfSe analysis identifying gut microbiota biomarkers distinguishing adult and young chickens. \u003cstrong\u003eJ\u003c/strong\u003e Top 10 taxa with the highest linear discriminant analysis (LDA) scores differentiating gut microbiota in adult and young chickens. Box and point graphs in (C)–(D) indicate the distribution of the data, the top of the box indicates upper quartile, the bottom of the box indicates the lower quartile, the line in the box indicates median, the top line beyond the box indicates maximum, the bottom line beyond the box indicates minimum (*** indicates \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ns indicates not significant). The Student’s t test was used for each comparison.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6786224/v1/e138f141cb90ab537500a01c.jpg"},{"id":85385405,"identity":"9add2588-9cea-40d4-9b0d-ae947cd2121f","added_by":"auto","created_at":"2025-06-25 09:45:36","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":284375,"visible":true,"origin":"","legend":"\u003cp\u003eViromic analysis of gut samples from adult chickens, young chickens, and FMT-treated young chickens. \u003cstrong\u003eA\u003c/strong\u003e Partial least squares discriminant analysis (PLS-DA) showing differences in gut virome composition among the groups (\u003cem\u003en\u003c/em\u003e = 20). \u003cstrong\u003eB–E\u003c/strong\u003e Comparison of alpha diversity indices: Chao1 (B), Ace (C), Shannon (D), and Simpson (E) among the groups. \u003cstrong\u003eF\u003c/strong\u003e Heatmap showing the relative abundance of viral families across samples. \u003cstrong\u003eG\u003c/strong\u003e Heatmap showing the relative abundance of viral species across samples. Box and point graphs in (B)–(E) indicate the distribution of the data, the top of the box indicates upper quartile, the bottom of the box indicates the lower quartile, the line in the box indicates median, the top line beyond the box indicates maximum, the bottom line beyond the box indicates minimum (** or *** indicate \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 and \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, respectively; ns indicates not significant). The Student’s t test was used for each comparison.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6786224/v1/45a43fa001e0dbfaba879c16.jpg"},{"id":85385413,"identity":"8228952f-0ff6-474f-8635-56c50d160abd","added_by":"auto","created_at":"2025-06-25 09:45:36","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":386270,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolomic analysis of gut samples from adult chickens, young chickens, and FMT-treated young chickens. \u003cstrong\u003eA\u003c/strong\u003e Partial least squares discriminant analysis (PLS-DA) showing differences in gut metabolome composition among the groups (\u003cem\u003en\u003c/em\u003e = 20). \u003cstrong\u003eB–D\u003c/strong\u003e Volcano plots illustrating significantly altered metabolites between adult and young groups (B), between young-FMT and young groups (C), and between adult and young-FMT groups (D). \u003cstrong\u003eE\u003c/strong\u003e Venn diagram of differentially expressed metabolites among different comparisons. \u003cstrong\u003eF\u003c/strong\u003e Heatmap of metabolites relative expression level in each sample. \u003cstrong\u003eG\u003c/strong\u003e KEGG functions of differentially expressed metabolites between adult and young groups. \u003cstrong\u003eH\u003c/strong\u003eDifferential abundance scores of the top 10 significantly enriched metabolic pathways across comparison groups. Box and point graphs in (H) indicate the distribution of the data (*, **, ***, or *** indicate \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 and \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001, respectively; ns indicates not significant). The Student’s t test was used for each comparison.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6786224/v1/53ab7a39857436425d61ad9e.jpg"},{"id":85385410,"identity":"bfe226e1-01e0-4b4b-b2b6-8e0bb0335835","added_by":"auto","created_at":"2025-06-25 09:45:36","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":383173,"visible":true,"origin":"","legend":"\u003cp\u003eGut microbiome analysis in a persistent IBV infection model in young chickens. \u003cstrong\u003eA\u003c/strong\u003e Experimental workflow of the persistent IBV infection model: fecal samples were collected for 16S sequencing prior to IBV infection and at days 3, 5, and 7 post-infection. \u003cstrong\u003eB\u003c/strong\u003e Partial least squares discriminant analysis (PLS-DA) showing differences in gut microbiota composition across groups (\u003cem\u003en\u003c/em\u003e = 10). \u003cstrong\u003eC\u003c/strong\u003e Venn diagram illustrating shared and unique operational taxonomic units (OTUs) among the groups. \u003cstrong\u003eD\u003c/strong\u003e Stacked bar plots showing microbiome composition at the species level across groups. \u003cstrong\u003eE\u003c/strong\u003e Bar plots illustrating abundance changes of the top 10 most abundant microbes before and after IBV infection. \u003cstrong\u003eF\u003c/strong\u003e Heatmap showing fold changes in the top 10 abundant microbes at 3, 5, and 7 dpi relative to pre-infection levels. \u003cstrong\u003eG\u003c/strong\u003e LEfSe analysis identifying significant microbial biomarkers between pre-infection and day 7 post-infection. \u003cstrong\u003eH\u003c/strong\u003e Top 10 taxa with the highest linear discriminant analysis (LDA) scores differentiating the gut microbiota between pre-infection and 7 dpi. Box and point graphs in (E) indicate the distribution of the data (* indicates \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ns indicates not significant). The Student’s t test was used for each comparison.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6786224/v1/72f2109bd5b213b16681478e.jpg"},{"id":85386421,"identity":"1ce501a0-be1e-435b-a34d-7161d76b2456","added_by":"auto","created_at":"2025-06-25 09:53:36","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":360078,"visible":true,"origin":"","legend":"\u003cp\u003eOral administration of \u003cem\u003eA. muciniphila\u003c/em\u003e significantly enhances resistance to IBV infection in young chickens. \u003cstrong\u003eA\u003c/strong\u003e Schematic of the experimental design: young chickens were orally administrated with \u003cem\u003eC. porcorum\u003c/em\u003e, \u003cem\u003eN. timonensis\u003c/em\u003e, or \u003cem\u003eA. muciniphila\u003c/em\u003e followed by IBV challenge. \u003cstrong\u003eB\u003c/strong\u003e Survival curves of young chickens in different groups (\u003cem\u003en\u003c/em\u003e = 20) after IBV challenge. \u003cstrong\u003eC\u003c/strong\u003e Bar plots summarizing survival rates across groups after IBV challenge. \u003cstrong\u003eD\u003c/strong\u003eNecropsy images of the trachea and kidneys at 5 dpi (Scale bar, 100 μm). \u003cstrong\u003eE\u003c/strong\u003eImmunohistochemistry (IHC) analysis of the trachea and kidneys at 5 dpi (Scale bar, 100 μm). \u003cstrong\u003eF–G\u003c/strong\u003e Viral loads in the kidney (F) and trachea (G) of young chickens from different groups (\u003cem\u003en\u003c/em\u003e = 5) on days 3, 5, and 7 post-IBV infection following oral gavage of \u003cem\u003eA. muciniphila\u003c/em\u003e. \u003cstrong\u003eH\u003c/strong\u003e Heatmap of cytokine expression levels in blood samples collected on day 7 post-IBV infection from young chickens in different groups following oral gavage of \u003cem\u003eA. muciniphila\u003c/em\u003e. \u003cstrong\u003eI\u003c/strong\u003e PLS-DA plot of metabolomic profiles in fecal samples collected on day 7 post-IBV infection from young chickens following oral gavage of \u003cem\u003eA. muciniphila\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e = 10). \u003cstrong\u003eJ\u003c/strong\u003e Bar plot of differentially expressed metabolites between the \u003cem\u003eA. muciniphila\u003c/em\u003e- administrated group and the IBV-infected group treated with antibiotics (Abx). \u003cstrong\u003eK\u003c/strong\u003e Volcano plot of differentially expressed metabolites between the \u003cem\u003eA. muciniphila\u003c/em\u003e-administrated group and the abx-treated IBV-infected group. The Kaplan-Meier (K-M) analysis was used to analyze the significance between groups in graph (B) (*, ** or *** indicate \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, respectively). Bar graphs in (D)–(E) represent the mean ± SEM, with error bars indicating the SEM (* indicates \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). The Student’s t test was used for each comparison.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6786224/v1/15107ff5cf9c96490317a939.jpg"},{"id":85386420,"identity":"319634ed-f9a5-4f92-9e0c-019ec1776bf8","added_by":"auto","created_at":"2025-06-25 09:53:36","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":303971,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eA. muciniphila\u003c/em\u003e enhances resistance to IBV infection in young chickens through GABA (γ-aminobutyric acid). \u003cstrong\u003eA\u003c/strong\u003eSchematic diagram of the animal experiment workflow, where young chickens were orally administrated of GABA followed by IBV infection. \u003cstrong\u003eB\u003c/strong\u003e Survival curves of young chickens in different groups (\u003cem\u003en\u003c/em\u003e = 20) following IBV infection. \u003cstrong\u003eC–F\u003c/strong\u003eExpression levels of IL-1β (C), IL-6 (D), TNF-α (E), and IFN-γ (F) in blood samples collected at day 7 post-IBV infection using ELISA kits (\u003cem\u003en\u003c/em\u003e = 5). \u003cstrong\u003eG\u003c/strong\u003eWestern blot analysis of nuclear translocation of p65 in chickens administrated of GABA and \u003cem\u003eA. muciniphila\u003c/em\u003e following IBV infection. \u003cstrong\u003eH–K\u003c/strong\u003e Relative abundance of IBV-N (H), p65 in total sample (I), p65 in cytoplasm sample (J), and p-p65 in nucleus sample (K) (\u003cem\u003en\u003c/em\u003e = 3). The Kaplan-Meier (K-M) analysis was used to analyze the significance between groups in graph (B) (* or *** indicate \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 and \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, respectively). Bar graphs in (C)–(F) and (H)–(K) represent the mean ± SEM, with error bars indicating the SEM (**, ***, or **** indicate \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, and \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001, respectively). The Student’s t test was used for each comparison.\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6786224/v1/889a61896a06db3ed95f709d.jpg"},{"id":86998398,"identity":"1e510cc6-4d25-4635-9764-c5501c298d2c","added_by":"auto","created_at":"2025-07-18 06:32:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3750618,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6786224/v1/3c809a5d-3987-4261-85ca-efe5f42f6a74.pdf"},{"id":85385407,"identity":"85474ef6-9a63-43f4-ab82-15d1deff5c2e","added_by":"auto","created_at":"2025-06-25 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09:45:37","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":14480899,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6786224/v1/c16e3a4bcca16c697bbf5777.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Akkermansia muciniphila Enhances Resistance to Infectious Bronchitis Virus in Chickens Through γ-Aminobutyric Acid-Mediated Anti-Inflammatory Pathways","fulltext":[{"header":"Background","content":"\u003cp\u003eThe gut microbiota plays an essential role in maintaining host health, exerting its influence on host physiology through diverse and multifaceted mechanisms [1, 2]. A key feature of the gut microbiota is its core functional groups, which exist in a dynamic and reciprocal equilibrium with host and environmental factors [3]. Dysbiosis of the gut microbiota has been implicated in the onset and progression of several diseases, including Crohn’s disease, type 2 diabetes, colorectal cancer [4-7], as well as viral infections such as COVID-19 and influenza [8, 9]. Conversely, deciphering the complex interactions between the gut microbiota and the host has paved the way for novel therapeutic interventions targeting specific microbial populations [10-12]. For example, microbial metabolites such as short-chain fatty acids (SCFAs) can enhance the immune responses of CD8\u003csup\u003e+\u003c/sup\u003e T cells [13] and have the potential to benefit animal bone health [14]. Large-scale clinical trials have demonstrated that probiotics can restore gut microbiota homeostasis and alleviate post-COVID-19 syndrome [15]. These findings underscore the transformative potential of leveraging microbiota-host interactions to develop innovative strategies for preventing and treating diseases with limited control measures.\u003c/p\u003e\n\u003cp\u003eThe gut microbiome has also been linked to the growth performance, development, and overall health of domestic avian species such as chickens and ducks [16, 17]. However, the specific contributions of gut microbiota to the pathogenesis and progression of poultry diseases remain largely unexplored. In China, the yellow-feathered broilers represent a major poultry breed, with an annual production volume approaching 4 billion birds [18]. Nonetheless, despite their economic importance, these birds remain highly susceptible to infectious bronchitis virus (IBV)\u0026nbsp;[19]. IBV is transmitted primarily through aerosols, exhibits broad tissue tropism, and induces severe disease with high mortality rates in young chickens and reproductive tract damage in adult chickens\u0026nbsp;[19, 20]. As a result, IBV inflicts annual economic losses amounting to millions of dollars and remains one of the most devastating pathogens threatening the global poultry industry\u0026nbsp;[21].\u0026nbsp;Like other coronaviruses, IBV is characterized by a high mutation rate, strong inflammatory induction, and immune evasion strategies\u0026nbsp;[22-24]. Moreover, its extensive genetic and serotypic diversity complicates vaccine development, as existing vaccines often fail to provide cross-protection against emerging variants, leading to frequent immune failures\u0026nbsp;[25-27]. At present, no approved therapeutics are available for the effective treatment of IBV in clinical settings\u0026nbsp;[28], emphasizing the urgent need for innovative and effective control strategies.\u003c/p\u003e\n\u003cp\u003eRecent studies suggest that modulating gut microbiota represents a promising avenue for combating viral infections, including those caused by SARS-CoV-2 and flaviviruses\u0026nbsp;[15, 29]. Despite these advancements, the role of gut microbiota in mitigating the transmission and infection of IBV remains insufficiently explored. In this study, we applied multi-omics approaches, integrating microbiomics, viromics, and untargeted metabolomics, to elucidate the gut microbiota-mediated mechanisms underlying why adult yellow-feathered chickens (60-70 days) exhibit higher resistance to IBV compared to young yellow-feathered chickens (10-20 days). Our findings identified \u003cem\u003eAkkermansia muciniphila\u0026nbsp;\u003c/em\u003e(\u003cem\u003eA. muciniphila\u003c/em\u003e) as the key microbial species conferring IBV resistance, with \u003cem\u003eCloacibacillus porcorum\u003c/em\u003e and \u003cem\u003eNeglecta timonensis\u003c/em\u003e exhibiting more limited but supportive roles. Using \u003cem\u003ein vivo\u003c/em\u003e experiments, we demonstrated that \u003cem\u003eA. muciniphila\u003c/em\u003e significantly improves IBV resistance in young chickens. Furthermore, mechanistic investigation revealed that \u003cem\u003eA. muciniphila\u003c/em\u003e exerts its protective effects by attenuating inflammation through the GABA-NF-κB signaling pathway and stimulating antiviral interferon production, ultimately boosting survival rates. Collectively, these findings provide a mechanistic basis for microbiota-mediated IBV resistance and introduce a novel live biotherapeutic products (LBPs)-based strategy for IBV prevention and mitigation, with broader implications for the development of therapeutics against other viral diseases.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eClinical samples and experimental animals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical samples from diseased chickens were obtained from poultry farm owners cooperated with Wen’s Food Group and Harbin Veterinary Research Institute (HVRI). Samples, including trachea, kidneys, and other diseased tissues, were collected from diseased or dead chickens and stored at −80 °C until use. For in vivo experiments, yellow-feathered broilers were purchased from collaborating farmers of Wen’s Food Group, and specific pathogen-free (SPF) chickens were obtained from Guangdong Wens Dahuanong Biotechnology Co., Ltd.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfectious bronchitis virus (IBV) propagation and challenge model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe QX-type IBV strain H24 was isolated and stored in our laboratory. IBV was propagated in 9- to 11-day-old SPF embryonated chicken eggs at 37 °C with 65% humidity. Inoculated eggs were monitored daily by candling for embryonic mortality. After 48 hours, the allantoic fluid was harvested, and the virus titer was determined using the 50% egg infective dose (EID\u003csub\u003e50\u003c/sub\u003e) method as described previously [30]. For the conventional infection model, the IBV challenge dose was 10\u003csup\u003e5\u003c/sup\u003e EID\u003csub\u003e50\u003c/sub\u003e, while in the low-dose infection model, the IBV challenge dose was 10\u003csup\u003e3\u003c/sup\u003e EID\u003csub\u003e50\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreparation and transplantation of chicken gut microbiota suspension\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the influence of adult and young chicken gut microbiota on IBV resistance in yellow-feathered broilers, fresh feces were collected from adult chickens (60-70 days old) and young chickens (10-20 days old). The microbiota suspension was prepared under anaerobic conditions as described previously [31]. Feces were homogenized and diluted with sterile saline, then filtered through sieves of 10, 18, 35, and 60 mesh. The filtrate was centrifuged at 6000 × g for 15 minutes, and the pellet was resuspended in sterile saline with 20% glycerol to a final concentration of 1 × 10⁹ CFU/mL.\u003c/p\u003e\n\u003cp\u003eTwo hundred 1-day-old SPF chickens were randomly divided into five groups (40 chickens per group: 20 for mortality observation and 20 for sample collection). The Abx-IBV, Abx-FMT(A)-IBV, and Abx-FMT(Y)-IBV groups were treated with antibiotics (ABX) in drinking water for five consecutive days. ABX doses were: vancomycin (80 mg/kg), neomycin (300 mg/kg), metronidazole (200 mg/kg), ampicillin (0.2 mg/kg), and colistin (24 mg/kg), with water changed every two days. One day after ABX cessation, the Abx-FMT(A)-IBV and Abx-FMT(Y)-IBV groups received 0.2 mL of fecal suspension from adult and young chickens, respectively, via oral gavage. This transplantation was repeated after three days, and IBV infection was conducted one day later.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCollection of fecal samples for multi-omics sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo analyze gut microenvironment differences among adult (Adult), young (Young), and young chickens transplanted with adult microbiota (Young-FMT), fresh fecal samples were collected from 60 adult chickens, 60 young chickens, and 60 Young-FMT chickens. For each group, samples from three chickens were pooled to reduce individual variation, resulting in 20 replicates per group. Samples were immediately frozen in liquid nitrogen and stored at −80 °C for 16S rRNA gene, viromics, and untargeted metabolomics sequencing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis pipeline for 16S rRNA gene amplicon sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first step is to use iTools (v.0.25) for quality control of the raw data. Reads that matched primers were selected by cutadapt v2.6 software to remove primer and joint contamination, and fragments of the target region were obtained. Then, removing reads that are contaminated by adapter sequences. Finally, readfq (v1.0) was used to remove reads with ambiguous base and low-complexity reads. The second step is to connect Tags and OTU clustering. The first approach utilizes Usearch, which clusters sequences based on 97% similarity to generate operational taxonomic units (OTUs). When using the Usearch method, paired-end reads with overlapping regions are assembled into a consensus sequence using FLASH (v1.2.11). The assembly requires a minimum overlapping length of 15 bp and allows a maximum mismatching ratio of 0.1 in the overlapping region. Tags are clustered into OTU with a 97% threshold by UPARSE, where the unique OTU representative sequences can be obtained. Then, chimeras in OTU are screened and filtered by mapping to gold database (v20110519) by UCHIME (v4.2.40). All tags are mapped to OTU representative sequences using USEARCH GLOBAL to calculate OTU abundance table. The second approach employs DADA2, which generates amplicon sequence variants (ASVs) by clustering denoised sequences with 100% similarity. The filtered paired-end sequences are imported using the qiime tools import command. Then, the qiime dada2 denoise-paired command is used to construct the feature table of the imported paired-end sequences based on DADA2. Finally, the feature table is converted into a directly viewable format using qiime tools export. The third step is to annotate OTU taxonomy. OTU representative sequences are aligned against the database for taxonomic annotation by RDP classifer (v2.2) software (sequence identity is set to be 0.6). Then removing OTU that are not annotated and those taxonomies that do not match with the project’s research background. In the fourth step, mothur (v.1.31.2) was used to analyze the Alpha diversity of species in a single sample, and the parameters included Chao1 index, Ace index, Shannon index, Simpson index, etc. The fifth step is to use QIIME (v1.80) for Beta diversity analysis. The fifth step is to use PICRUST2 to predict the function of bacterial community.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLinear discriminant analysis effect size (LEfSe) analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLEfSe analysis was performed on the platform Galaxy (https://usegalaxy.org/). Select the structure of the problem (classes, subclasses, and subjects) and format the tabular abundance data. Perform the analysis using the formatted tabular abundance data. Graphically report the discovered biomarkers along with their effect sizes. Represent the discovered biomarkers in a taxonomic tree based on hierarchical feature names. Plot the raw values of a single feature as an abundance histogram structured by classes and subclasses. Plot the raw values of all features as abundance histograms structured by classes and subclasses, and provide a zip archive containing the figures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeta-transcriptomic virome analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHecatomb is used for meta-transcriptomic virome analysis [32]. The analytical process begins with stringent preprocessing to ensure that only non-contaminant biological sequences are retained for downstream analysis. This includes the removal of non-biological sequences such as primers and adapters, host sequence depletion, redundancy reduction through clustering, sequence count table generation, and the calculation of sequence properties such as GC content and tetramer frequencies. A predefined set of host genomes is available for this step, with the option to incorporate custom genomes as needed.\u003c/p\u003e\n\u003cp\u003eTaxonomic classification is performed through an iterative search strategy using MMSeqs2. Initially, query sequences from the preprocessing step are aligned against a virus-specific protein database in a translated nucleotide-to-protein search. Sequences assigned a viral taxonomy at this stage undergo a secondary validation against a broader reference database encompassing bacteria, plants, vertebrates, fungi, and other domains of life. This step is necessary to eliminate false positives arising from the restricted nature of the initial viral protein database. Although directly performing the secondary search would be more comprehensive, it is computationally prohibitive due to the vast size of general sequence databases. Thus, the two-step approach efficiently captures potential viral sequences while refining classification accuracy.\u003c/p\u003e\n\u003cp\u003eFor population-level analysis, assembly is performed to generate contigs, which serve as a foundation for subsequent investigations. This process employs a resource-efficient strategy designed to maximize species representation. First, individual assemblies are generated using MEGAHIT or Canu for each sample. Reads are mapped back to these assemblies, and any unmapped reads undergo an additional round of assembly. The resulting contigs are merged into a non-redundant dataset using Flye. These contigs are then annotated with MMSeqs, while a complementary pseudo-consensus annotation is performed by integrating read mapping data with taxonomic assignments from the sequence table. This approach enhances the resolution of contig origins and facilitates downstream virome analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample preparation for metabolome analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter thawing the sample slowly at 4 °C, weigh 25 mg and place it into a 1.5 mL Eppendorf tube. Add 800 μL of extraction solution (methanol: acetonitrile: water = 2:2:1, v:v:v, pre-cooled to -20 °C) and 10 μL of internal standard. Add two small steel balls and grind the sample using a tissue grinder (50 Hz, 5 min). Perform ultrasound treatment in a 4 °C water bath for 10 min. Incubate the sample in a refrigerator at 20 °C for 1 hour. Centrifuge at 25,000 g for 15 min at 4 °C. After centrifugation, transfer 600 μL of the supernatant into a freeze vacuum concentrator to dry. Then, re-dissolve the residue in 600 μL of complex solution (methanol: H₂O = 1:9, v:v). Vortex for 1 min, perform ultrasound treatment in a 4 °C water bath for 10 min, and centrifuge at 25,000 g for 15 min at 4 °C. Transfer the supernatant into the sample vial. For quality control, mix 50 μL of supernatant from each sample to create QC samples, which are used to evaluate the repeatability and stability of the LC-MS analysis process. The sample extracts were analyzed using a Waters UPLC I-Class Plus (Waters, USA) equipped with a QTRAP 6500 Plus mass spectrometer (SCIEX, USA). After importing the offline mass spectrometry data into Compound Discoverer 3.3 (Thermo Fisher Scientific, USA) and analyzing it using the BMDB (BGI Metabolome Database), mzCloud database, and ChemSpider online database, a data matrix containing information such as metabolite peak areas and identification results was generated. The table was then further analyzed and processed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReverse transcription quantitative polymerase chain reaction (RT-qPCR)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA of the kidney, trachea, and ileum at 3, 5 and 7 dpi were extracted by TRIzol Reagent (Invitrogen Life Technologies) and reverse transcribed using a ReverTra Ace® qPCR RT Master Mix with gDNA Remover kit (TOYOBO, Osaka, Japan). Relative quantitative PCR was performed for the detection of the N gene [33] using Hieff™ qPCR SYBR® Green Master Mix (Yeasen, Shanghai, China) on a Light-Cycler 480 PCR system (Roche, Basel, Switzerland), and the GAPDH gene was used as the reference gene for normalization. The cycling conditions were as follows: 95 ℃ for 5 min, followed by 40 cycles of 95 °C for 10 s, 58 °C for 10 s, and 72 °C for 30 s, with subsequent incubations at 95 °C for 5 s, 60 ℃ for 1 min and 95 °C for 15 s. Data were analyzed using the 2\u003csup\u003e−ΔΔCT\u003c/sup\u003e method and presented as the change (n-fold) relative to either the control group inoculated with PBS or non-infected cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWestern blot\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCollected chicken kidney tissues were denaturated and lysed after different hours post infection. The samples were fractionated by electrophoresis on 12 % SDS-PAGE gels, and resolved proteins were transferred onto polyvinylidene difluoride (PVDF) membranes. After blocking with 5% Bovine Serum Albumin (BSA), the membranes were incubated with anti IBV-N mouse monoclonal antibody (MyBioSource) in 1 % BSA in TBST for 1 h, followed by HRP-conjugated goat anti-mouse IgG(H+L) secondary antibody (Proteintech).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHistopathology and immunohistochemistry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe trachea and kidney tissues were fixed, dehydrated, transparentized, and embedded in paraffin. After fixation and decalcification, the tissues were sectioned to 5 μm. H\u0026amp;E staining, and immunohistochemistry were performed separately according to the instructions of the reagent. The section was captured using a Nikon TS2 microscope (Nikon Instech, Tokyo, Japan).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt2)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor functional profiling, we employed Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUSt2) [34], a bioinformatics tool that predicts microbial gene functions based on 16S rRNA gene sequencing data. This tool infers potential metabolic pathways encoded by the microbiota by leveraging reference genome databases. \u0026nbsp;The functional annotations of bacterial gene functions were performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Differential abundance analysis of both bacterial taxa and predicted gene functions was conducted using STAMP, with statistical significance determined at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnzyme-linked immunosorbent assay (ELISA) for cytokine quantification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerum levels of inflammatory cytokines were quantified using commercial ELISA kits, following the manufacturers’ protocols. Absorbance measurements were recorded at 450 nm using a microplate reader (Bio-Rad, USA). Specifically, IL-16, IFN-α, IFN-γ, IL-10, IL-21, IL-2, IL-6, and CCL4 were analyzed using the Millipore MILLIPLEX Chicken Cytokine/Chemokine Panel 1 ELISA kit (GCYT1-16K-PX12). Additionally, IL-1β (CSB-E11230Ch), IFN-β (CSB-E11237Ch), and TNF-α (CSB-E11231Ch) levels were determined using separate ELISA kits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMantel test for correlation analysis of microbiome, virome, and metabolome\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate correlations among microbiome, virome, and metabolome datasets, a Mantel test was performed, and the relationships were visualized using a correlation heatmap. The microbiome, virome, and metabolome datasets were imported into R, where Pearson correlation coefficients were computed for environmental factors. Statistically significant correlations were extracted and stored for further analysis. The Mantel test was applied to assess inter-dataset associations, with results categorized and visualized. A correlation heatmap was generated, highlighting statistically significant Pearson correlation values. Additionally, Mantel test results were overlaid on the heatmap using connecting lines of varying thickness and color to represent correlation strength and significance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction of bacterial phage host range using integrated phage host prediction (iPHoP)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eiPHoP (Integrated Phage HOst Prediction) was employed to predict the host range of bacterial phages [35]. All assembled phage contigs were selected and imported into iPHoP in FASTA format. The analysis was conducted using default parameters, leveraging BLAST, CRISPR, and iPHoP-RF methodologies for host prediction. The resulting host predictions were subsequently imported into R for downstream visualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction and visualization of the microbiome-virome interaction network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo construct and visualize microbiome-virome interactions, raw microbiome and virome abundance data were uploaded to http://cloudtutu.com.cn/. Pearson’s correlation analysis was conducted to assess relationships between microbiomic and viromic datasets, applying a filtering criterion of |r| ≥ 0.5 and \u003cem\u003ep\u003c/em\u003e ≤ 0.05. The resulting correlation data were exported and imported into R for network visualization. A microbiome-virome interaction network was constructed using a force-directed layout algorithm. The network graph was plotted with annotations indicating modular structures, node classifications, edge weights, and node abundances to enhance interpretability.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eAge-dependent susceptibility to IBV highlighted by clinical and experimental insights\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on a decade-long epidemiological survey conducted by our research team, IBV exhibits a significantly higher mortality rate in younger chickens compared to older ones. Among adult chickens, the mortality rate caused by IBV was observed to be only 4.8%. In contrast, the mortality rate in young chickens reached 29.8% (Fig. 1A). The epidemic IBV strains were isolated, propagated, and genotyped, revealing that QX-like IBV is the most prevalent field strain, accounting for 53.1% of isolates (Table S1). To validate these clinical observations, we established an IBV infection model using the predominant QX-like genotype strain to infect young (10\u0026ndash;20 days old) and adult (60\u0026ndash;70 days old) yellow-feathered broilers. The results revealed that IBV caused a mortality rate of 10% in adult chickens but 60% in adult chickens (Fig. 1B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRT-qPCR analysis of viral loads in the kidney, trachea, and ileum on 3, 5, and 7 days post infection (dpi) demonstrated significantly higher viral loads in the kidneys of young chickens compared to adult chickens on 5 and 7 dpi (Fig. 1C). However, no significant differences were observed in the viral loads of the trachea and ileum (Fig. S1A,B). On the 5 dpi, clinical autopsy showed no apparent symptoms in adult chickens, whereas young chickens exhibited hallmark symptoms, including tracheal hemorrhage and mottled kidneys (Fig. S1C). Histopathological analysis revealed severe hemorrhage and inflammatory cell infiltration in the kidneys of young chicken post-infection (Fig. S1D), immunohistochemistry (IHC) analysis demonstrated higher viral loads of kidney and trachea in young chickens than that in adult chickens (Fig. S1E). Collectively, these findings corroborate our clinical observations that IBV causes more severe clinical symptoms and higher mortality rates in younger chickens compared to adults.\u003c/p\u003e\n\u003cp\u003eTo investigate whether the differences in IBV-induced mortality rates between young and adult chickens could be attributed to gut microbiota, we developed a gut microbiota transplantation model (Fig. 1D). Fecal microbiota from adult or young chickens were transplanted into 6-day-old recipient specific pathogen free (SPF) young chickens pre-treated with antibiotics for five days. The recipient young chickens were subsequently challenged with QX genotype IBV at 10 days of age and monitored for 15 days post-infection (Fig. 1D). The results demonstrated that transplantation of adult chicken fecal microbiota significantly improved the survival rates of recipient young chickens following IBV infection, whereas transplantation of young chicken fecal microbiota had no significant impact on recipient survival (Fig. 1E).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, the young chickens that receiving adult chicken fecal microbiota exhibited significantly lower viral loads in their trachea, kidney, and ileum compared to those receiving young chicken fecal microbiota (Fig. 1F,G and Fig. S2A,D), and the pathological damage induced by IBV in both the kidney and trachea has also been markedly reduced (Fig. S2B,C), and the growth performance was also restored (Fig. 1H). These findings confirm that gut microbiota can influence IBV resistance in young chickens.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGut microbial profiling reveals divergent community structures across adult, young, and young-FMT chickens\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe gut microenvironment is a complex ecosystem comprising diverse bacteria, viruses, and metabolites that collectively regulate host immunity and health [36]. To elucidate the mechanisms underlying the differential effects of the gut microbiota from adult chickens on IBV resistance in young chickens, we employed an integrative approach combining microbiomics, viromics, and untargeted metabolomics to characterize the gut microenvironment across these groups.\u003c/p\u003e\n\u003cp\u003e16S rRNA gene sequencing was used to detect the chicken gut microbiota. Partial least squares discrimination analysis (PLS-DA) of microbiomic sequencing data demonstrated robust group clustering and significant divergence in gut microbial composition among adult chickens, young chickens, and young chickens treated with adult chicken fecal microbiota transplantation (young-FMT) (Fig. 2A). Among the 1,662 operational taxonomic units (OTUs) identified, 804 were shared across the three groups, whereas 1,390 OTUs (83.6%) were shared between adult and young-FMT chickens, suggesting efficient colonization of adult chicken microbiota in young chicken guts following FMT (Fig. 2B and Fig. S3). Alpha diversity analysis further revealed that the gut microbiota of adult and young-FMT chickens exhibited significantly greater diversity compared to young chickens (Fig. 2C,D and Fig. S4A,B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTaxonomic classification at the phylum, class, order, family, genus, and species levels revealed substantial differences in microbial composition across three groups (Table S2), the gut microbiota of young-FMT chickens closely resembled that of adult chickens at nearly all taxonomic levels (Fig. 2E\u0026ndash;H and Fig. S4C\u0026ndash;J). At the class level, young chickens exhibited a predominance of \u003cem\u003eClostridia\u003c/em\u003e (76.8%), which was considerably higher than in adult (42.8%) and young-FMT chickens (64.1%). In contrast, adult and young-FMT chickens showed significantly higher abundances of \u003cem\u003eSynergistia\u003c/em\u003e (22.0% and 17.2% vs. \u0026lt;0.1%), \u003cem\u003eDeltaproteobacteria\u003c/em\u003e (4.5% and 0.9% vs. 0.2%), and \u003cem\u003eCoriobacteriia\u003c/em\u003e (1.8% and 1.0% vs. 0.5%)(Fig. 2E). Linear discriminant analysis effect size (LEfSe) analysis highlighted top 10 taxa enriched in adult chickens versus to at the genus level, including \u003cem\u003eCloacibacillus\u003c/em\u003e, \u003cem\u003eDesulfovibrio\u003c/em\u003e, \u003cem\u003eMegasphaera\u003c/em\u003e, \u003cem\u003eAkkermansia\u003c/em\u003e, and \u003cem\u003eThermophilibacter\u003c/em\u003e (Fig. 2I,J). The top 10 genus in young-FMT chickens versus to young chickens including \u003cem\u003eCloacibacillus\u003c/em\u003e, \u003cem\u003eAkkermansia\u003c/em\u003e, and\u003cem\u003e\u0026nbsp;Mediterranea\u0026nbsp;\u003c/em\u003e(Fig. S5A,B). In contrast, the microbiota differences between adult and young-FMT chickens were minimal (Fig. S5C,D).\u003c/p\u003e\n\u003cp\u003eFunctional predictions using PICRUSt2 further revealed distinct microbial metabolic pathway enrichments between adult and young chickens. The gut microbiota of adult chickens was particularly enriched in pathways associated with energy metabolism and digestive system functions (Fig. S6 and Table S3). Collectively, these findings provide a comprehensive framework for understanding the unique gut microbial compositions and functional capacities of adult, young, and young-FMT chickens, emphasizing their roles in differential IBV resistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetatranscriptomic insights into gut virome dynamics in adult, young, and young-FMT chickens\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe gut virome, a critical component of the intestinal microenvironment, was analyzed in this study using metatranscriptomic approach. PLS-DA results revealed high intra-group reproducibility and distinct clustering of virome structures among adult chickens, young chickens, and young-FMT chickens (Fig. 3A). Alpha diversity analysis further demonstrated that adult and young-FMT both harbored a richer virome compared to young chickens, while less significant differences were observed between adult and young-FMT chickens (Fig. 3B\u0026ndash;E).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTaxonomic annotation of sequencing reads revealed substantial differences in viral family abundances. At the phylum level, \u003cem\u003eParvoviridae\u003c/em\u003e, \u003cem\u003eSmacovirida\u003c/em\u003ee, \u003cem\u003eCircoviridae\u003c/em\u003e, \u003cem\u003eMicroviridae\u003c/em\u003e, and \u003cem\u003ePicobirnaviridae\u003c/em\u003e were more prevalent in adult and young-FMT chickens, whereas \u003cem\u003eSedoreoviridae\u003c/em\u003e, \u003cem\u003eAckermannviridae\u003c/em\u003e, \u003cem\u003eCoronaviridae\u003c/em\u003e, and \u003cem\u003ePicornaviridae\u003c/em\u003e dominated in young chickens (Fig. 3F). At the genus level, adult and young-FMT chickens showed higher abundances of \u003cem\u003ePicobirnavirus\u003c/em\u003e, \u003cem\u003eTremovirus\u003c/em\u003e, \u003cem\u003eAvisivirus\u003c/em\u003e, \u003cem\u003ePosavirus\u003c/em\u003e, and \u003cem\u003eMicrovirus\u003c/em\u003e, while \u003cem\u003eGammacoronavirus\u003c/em\u003e, \u003cem\u003eGallivirus\u003c/em\u003e, \u003cem\u003eRotavirus\u003c/em\u003e, and \u003cem\u003eMegrivirus\u003c/em\u003e were more abundant in young chickens (Fig. 3G). These differences extended across taxonomic ranks, highlighting consistent divergence in the gut virome composition between different ages of chickens (Fig. 3F,G; Fig. S7A,B and Table S4). Notably, the gut virome profiles of adult and young-FMT chickens displayed similar patterns, indicating effective virome colonization from adult chicken feces to the recipient young chickens following FMT.\u003c/p\u003e\n\u003cp\u003eAfter filtering low-quality contigs, assembly of viral reads yielded 1,083 high-quality viral contigs, ranging from 1,001 to 27,574 nucleotides in length, belonging to families such as \u003cem\u003ePicornaviridae\u003c/em\u003e, \u003cem\u003eCoronaviridae\u003c/em\u003e, and \u003cem\u003eAstroviridae\u003c/em\u003e (Fig. S8A). Phylogenetic analysis comparing these contigs with their respective reference genomes revealed significant genetic divergence, suggesting evolutionary adaptations within the gut virome (Fig. S8B\u0026ndash;D). Assembled DNA viruses were predominantly bacteriophages, which play a potential regulatory role in the gut microbial ecosystem. A total of 51 phage contigs were subjected to host prediction, including members of the families \u003cem\u003eMicroviridae\u003c/em\u003e, \u003cem\u003eHerelleviridae\u003c/em\u003e, and \u003cem\u003eAckermannviridae\u003c/em\u003e. Using the iPHoP software, which integrates BLAST, CRISPR, and iPHoP-RF algorithms, host prediction identified 13 phage contigs from \u003cem\u003eMicroviridae\u003c/em\u003e family targeting the genus \u003cem\u003eFaecalibacterium\u003c/em\u003e and 7 phage contigs \u003cem\u003eHerelleviridae\u003c/em\u003e family from targeting the genus \u003cem\u003eStercorousia\u003c/em\u003e (Fig. S9A\u0026ndash;C and Table S5).\u003c/p\u003e\n\u003cp\u003eMoreover, complex interactions between bacteria and viruses within the gut microenvironment were explored, as these interactions are known to regulate host health and disease outcomes [37]. Interplay networks encompassing bacteria-virus, bacteria-bacteria, and virus-virus interactions were constructed for adult and young chickens, respectively (Fig. S10, Table S6). In adult chickens, 2,581 interaction pairs were identified, while in young chickens, the number increased to 5,551. These findings provide a foundational understanding of the intricate avian gut microenvironment and its potential regulatory mechanisms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolomic characterization uncovers distinct gut metabolic pathways in adult, young, and young-FMT chickens\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGut cells and microbiota produce a diverse array of metabolites that play essential physiological roles, shaping a complex and dynamic gut microenvironment in coordination with bacteria and viruses. Untargeted metabolomic analysis identified a total of 3,115 gut metabolites in chickens, comprising 523 lipids (16.8%) and 414 amino acids, peptides, and analogs (13.3%) (Fig. S11A and Table S7). The primary functions of metabolites include amino acid metabolism, biosynthesis of other secondary metabolites, and lipid metabolism (Fig. S11B). Partial least squares discrimination analysis (PLS-DA) revealed high intra-group consistency and significant differences in gut metabolite profiles among adult, young, and young-FMT chickens, with adult and young-FMT chickens clustering more closely together than young chickens. (Fig. 4A).\u003c/p\u003e\n\u003cp\u003eA total of 2,239 differentially expressed metabolites were identified in adult chickens compared to young chickens, with 1,226 metabolites upregulated and 1,013 downregulated. Additionally, the differentially expressed metabolites between young-FMT and young chickens, as well as adult and young-FMT chickens, were also identified. (Fig. 4B\u0026ndash;D and Table S8). And the metabolic patterns of adult chickens gut significantly differ to that of young chickens, but exhibit a more similar trend compared to that of young-FMT chickens (Fig. 4E,F). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that these differentially expressed metabolites between adult and young were enriched in pathways related to neuroactive ligand-receptor interaction, ABC transporters and tryptophan metabolism (Fig. 4G and Table S9). The expression level of differentially expressed metabolites in the most significant KEGG pathway, neuroactive ligand-receptor interaction, revealed similar pattern between adult and young-FMT groups, which is different from young group (Fig. 4H).\u003c/p\u003e\n\u003cp\u003eCross-referencing enriched pathways from metabolomic data with those predicted by microbiome analyses identified 40 shared pathways, highlighting strong concordance between the two datasets (Fig. S11C). Correlation analysis using Mantel tests revealed statistically significant associations between gut metabolites and microbial families, such as \u003cem\u003eCampylobacteraceae\u003c/em\u003e (Mantel\u0026rsquo;s r \u0026gt; 0.5 and Mantel\u0026rsquo;s \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and between the virome and microbial taxa, such as\u003cem\u003e\u0026nbsp;Clostridiales\u0026nbsp;\u003c/em\u003eand \u003cem\u003ePeptococcaceae\u003c/em\u003e (Mantel\u0026rsquo;s r \u0026gt; 0.25 and Mantel\u0026rsquo;s \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01) (Fig. S11D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDynamic modulation of gut microbiota in a low-dose IBV infection model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh-dose infectious bronchitis virus (IBV) challenge models frequently result in substantial mortality among young chickens, hindering the ability to study longitudinal changes in gut microbiota. To overcome this limitation, a low-dose infection model was developed to track gut microbiota dynamics over time without inducing fatality. Fecal samples were collected pre-infection (0) and at 3, 5, and 7 days post-infection for 16S rRNA gene sequencing (Fig. 5A).\u003c/p\u003e\n\u003cp\u003ePLS-DA analysis revealed consistent intra-group clustering and significant shifts in gut microbial composition following IBV infection (Fig. 5B). Across all samples, 971 operational taxonomic units (OTUs) were shared between pre- and post-infection groups, reflecting core microbiota retention despite infection (Fig. 5C and Table S10). Alpha diversity analysis indicated a transient decline in microbial richness on days 3 and 5 post-infection, followed by partial recovery by day 7 (Fig. S12A\u0026ndash;D).\u003c/p\u003e\n\u003cp\u003eAt the species level, bacterial abundance exhibited dynamic fluctuations during infection. For instance, \u003cem\u003ePhocaeicola caecigallinarum\u003c/em\u003e showed an initial decline followed by recovery, whereas \u003cem\u003eAkkermansia muciniphila\u0026nbsp;\u003c/em\u003e(\u003cem\u003eA. muciniphila\u003c/em\u003e) displayed a continuous upward trend (Fig. 5D). Among the top 10 most abundant taxa, \u003cem\u003eA. muciniphila\u003c/em\u003e and \u003cem\u003eKineothrix alysoides\u003c/em\u003e demonstrated significant increases on days 7 and 3 post-infection, respectively (Fig. 5E and Fig. S12E). Notably, \u003cem\u003eA. muciniphila\u003c/em\u003e showed the highest fold-change, with abundance rising 6.3-fold and 6.6-fold on days 5 and 7, respectively (Fig. 5F).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLEfSe analysis identified distinct microbial signatures between pre-infection and day 7 post-infection samples, with \u003cem\u003eAkkermansia\u003c/em\u003e emerging as the most enriched genus on day 7 (Fig. 5G,H). This finding aligns with previous observations that \u003cem\u003eA. muciniphila\u003c/em\u003e is a key biomarker in the gut microbiota of adult and young-FMT chickens, where it is significantly more abundant than that in young chickens (Fig. 2H\u0026ndash;J and Fig. S4D,J). Additionally, two other species, \u003cem\u003eCloacibacillus porcorum\u003c/em\u003e (\u003cem\u003eC. porcorum\u003c/em\u003e) and \u003cem\u003eNeglecta timonensis\u003c/em\u003e (\u003cem\u003eN. timonensis\u003c/em\u003e), were upregulated following IBV infection and exhibited higher abundances in adult and young-FMT chickens compared to young chickens (Fig. S13A\u0026ndash;C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtective role of \u003cem\u003eA. muciniphila\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003ein enhancing young chickens\u0026rsquo;s resistance to IBV infection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe significant upregulation of \u003cem\u003eA. muciniphila\u003c/em\u003e, \u003cem\u003eC. porcorum\u003c/em\u003e, and \u003cem\u003eN. timonensis\u003c/em\u003e following IBV infection, alongside their higher abundance in the gut microbiota of adult and young-FMT chickens, prompted the hypothesis that these bacteria contribute to resistance of chickens against IBV. To test this, the three bacterial strains were procured, propagated, and administered individually to young chickens by oral gavage prior to IBV challenge (Fig. 6A).\u003c/p\u003e\n\u003cp\u003eAmong the treatments, \u003cem\u003eA. muciniphila\u003c/em\u003e markedly improved survival rates of chickens to 90%, compared to 65% for \u003cem\u003eC. porcorum\u003c/em\u003e and 50% for\u003cem\u003e\u0026nbsp;N. timonensis\u003c/em\u003e (Fig. 6B,C). These results establish \u003cem\u003eA. muciniphila\u003c/em\u003e as a key factor in conferring resistance to IBV in chickens, with \u003cem\u003eC. porcorum\u003c/em\u003e and\u003cem\u003e\u0026nbsp;N. timonensis\u003c/em\u003e providing moderate, yet limited, protective effects.\u003c/p\u003e\n\u003cp\u003eGiven the pronounced efficacy of \u003cem\u003eA. muciniphila\u003c/em\u003e, subsequent investigations focused on this bacterium. Oral administration of \u003cem\u003eA. muciniphila\u003c/em\u003e significantly reduced pathological damage and viral loads in the kidneys and trachea of infected young chickens by day 5 post-infection (Fig. 6D\u0026ndash;G and Fig. S14).\u003c/p\u003e\n\u003cp\u003eSerum analysis on 5 dpi revealed reduced levels of pro-inflammatory cytokines (IL-1\u0026beta;, IL-6, and IFN-\u0026gamma;) and elevated levels of antiviral interferons (IFN-\u0026alpha; and IFN-\u0026beta;) in \u003cem\u003eA. muciniphila\u003c/em\u003e-treated chickens (Fig. 6F). This immune modulation highlights the \u003cem\u003eA. muciniphila\u003c/em\u003e\u0026rsquo;s role in reducing inflammation and enhancing antiviral responses.\u003c/p\u003e\n\u003cp\u003eTo further elucidate the underlying mechanism, metabolomic profiling of gut samples collected on day 5 post-infection was conducted. The analysis identified 53 upregulated and 58 downregulated metabolites in\u003cem\u003e\u0026nbsp;A. muciniphila\u003c/em\u003e-treated and IBV-infected group compared to the IBV-infected group (Fig. 6I,J and Fig. S15A), the KEGG function of differentially expressed metabolites were also enriched (Fig. S15B). Among these, \u0026gamma;-aminobutyric acid (GABA) exhibited the highest fold increase (Fig. 6K). Consistent with prior findings, GABA levels were also significantly higher in the gut of adult chickens compared to young chickens (Fig. S14A\u0026ndash;B).\u003c/p\u003e\n\u003cp\u003eGABA, a four-carbon non-proteinogenic amino acid, is known for its critical roles in various physiological and biochemical processes across organisms [38]. Notably, GABA has been reported to regulate immune responses, including the inhibition of IL-1\u0026beta; production[39]. These observations suggest that \u003cem\u003eA. muciniphila\u003c/em\u003e enhances IBV resistance in young chickens by modulating GABA levels, which subsequently suppress IL-1\u0026beta; production and mitigate inflammation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGABA modulates NF-\u0026kappa;B signaling to enhance chick survival following IBV infection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo confirm the role of GABA in enhancing IBV resistance, we administered GABA to young chickens prior to IBV challenge and monitored their survival (Fig. 7A). GABA treatment significantly improved chick survival rates following IBV infection (Fig. 7B). Serum cytokine analysis on day 5 post-infection revealed that GABA administration markedly suppressed pro-inflammatory cytokines (IL-1\u0026beta;, IL-6, TNF-\u0026alpha;, and IFN-\u0026gamma;) while promoting antiviral interferons (IFN-\u0026alpha; and IFN-\u0026beta;) (Fig. 7C\u0026ndash;E and Fig. S17A,B). GABA also mitigated the pathological damage induced in kidney and trachea, and reduced the viral loads in these tissues (Fig. S17C,D).\u003c/p\u003e\n\u003cp\u003eIBV infection induces inflammation and tissue damage via activation of the NF-\u0026kappa;B signaling pathway [40]. GABA has been shown to inhibit inflammation by reducing IL-1\u0026beta; expression [39]. To investigate whether the anti-inflammatory effects of GABA are mediated through NF-\u0026kappa;B signaling, we examined p65 protein expression in the kidneys of infected young chickens on 5 dpi. GABA treatment significantly suppressed IBV-induced p65 phosphorylation and nuclear translocation, thereby mitigating NF-\u0026kappa;B-mediated inflammatory responses and kidney tissue damage (Fig. 7G\u0026ndash;K). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese findings confirm that GABA plays a pivotal role in regulating immune responses and inflammation during IBV infection, primarily by modulating the NF-\u0026kappa;B signaling pathway.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eA decade of clinical observations reveal that adult chickens exhibit stronger resistance to IBV than young chickens, but the role of the gut microbiome remain unclear. Given its crucial role in poultry development and health [41, 42] and the industry’s shift toward antibiotic-free farming [43, 44], understanding the role of gut microbiome in IBV resistance is of both scientific and practical significance. In this study, we demonstrate that FMT from adult chickens significantly enhances IBV resistance in young chickens. Using an integrated multi-omics approach, we systematically characterize the gut microbiomic, viromic, and metabolomic profiles of adult, young, and young-FMT chickens, showing that FMT-treated young chickens developed a microbiota composition closely resembling that of adults. Further investigations using a novel persistent IBV infection model identifies \u003cem\u003eA. muciniphila\u003c/em\u003e, \u003cem\u003eC. porcorum\u003c/em\u003e, and \u003cem\u003eN. timonensis\u003c/em\u003e as key probiotic candidates, with \u003cem\u003eA. muciniphila\u003c/em\u003e exhibiting the strongest protective effect. Mechanistic study reveals that \u003cem\u003eA. muciniphila\u003c/em\u003e enhances IBV resistance by increasing GABA production, which suppresses NF-κB-driven inflammation and upregulates antiviral interferon expression. These findings establish \u003cem\u003eA. muciniphila\u003c/em\u003e as a promising probiotic for IBV control and highlight microbiota modulation as a viable strategy for enhancing antiviral immunity in poultry.\u003c/p\u003e\n\u003cp\u003eIBV is one of the most significant viral pathogens affecting the global poultry industry, causing millions of dollars in economic losses due to its high prevalence, broad tissue tropism, and severe morbidity and mortality rates\u0026nbsp;[21]. IBV is one of the most significant viral pathogens affecting the global poultry industry, causing millions of dollars in economic losses due to its high prevalence, broad tissue tropism, and severe morbidity and mortality rates [45]. However, their efficacy is limited by inadequate cross-protection against diverse IBV variants [45, 46]. Moreover, the absence of approved antiviral drugs, coupled with restrictions on drug use in poultry industry, underscores the need for alternative strategies that align with antibiotic-free and sustainable farming policies. While previous studies have demonstrated the critical role of gut microbiota in IBV resistance [47], its potential as a therapeutic intervention remains unclear. Here, we confirm that FMT from adult chickens significantly improves the survival rate of young chickens following IBV infection (Fig. 1D, E), a crucial finding given that IBV causes the most severe damage in young birds [45]. Additionally, FMT reduces viral loads (Fig. 1F,G\u0026nbsp;and Fig.\u0026nbsp;S2D) and mitigates tissue damage in the kidneys and trachea (Fig.\u0026nbsp;S2B,C), the primary target organs of IBV [48]. FMT has been demonstrated as a promising strategy for significantly enhancing antioxidant capacity, immune function, and intestinal glucose transport in young chickens [41].Our findings highlight FMT as a promising microbiome-based therapeutic strategy for IBV control and a sustainable alternative for poultry disease management in the context of antibiotic-free farming.\u003c/p\u003e\n\u003cp\u003eBy integrating multi-omics analyses, we comprehensively characterized the gut microbiome of adult chickens, young chickens, and young chickens treated with FMT. Compared to young chickens, young-FMT chickens exhibited a significant decrease in \u003cem\u003eBacillota\u003c/em\u003e and an increase in \u003cem\u003eSynergistota\u003c/em\u003e, mirroring the microbial composition of adult donor chickens (Fig. S4C). This shift indicates the successful colonization of transplanted microbiota. Previous studies have similarly reported that FMT reshapes the gut microbiome of recipient chickens, aligning it closely with that of donors [49]. However, while FMT transfers not only bacteria but also viruses and metabolites, little research has explored these additional components. In this study, we further examined viromic and metabolic profiles post-FMT and found that young-FMT chickens exhibited viral (Fig. 3F,G) and metabolic (Fig. 4F) compositions similar to those of adult donors, distinguishing them from untreated young chickens. While prior research has shown that FMT alters the metabolome of recipients, it has not directly compared these changes with donor metabolic profiles [50, 51]. Our results suggest that FMT facilitates not only beneficial bacterial colonization but also the transfer of advantageous metabolites, such as GABA (Fig. 4H). However, we also observed an increased relative abundance of IBV in young-FMT chickens compared to untreated young chickens, potentially due to viral transmission from adult donors (Fig. 3G). This concern aligns with warnings from the U.S. Food and Drug Administration (FDA) regarding the risk of transmitting SARS-CoV-2 during FMT procedures [52]. Therefore, to ensure the safety of FMT-based interventions in poultry, stringent screening of FMT donors for pathogenic viruses is essential to mitigate potential transmission risks.\u003c/p\u003e\n\u003cp\u003eIn a newly established persistent IBV infection model, we identified three candidate probiotics, \u003cem\u003eA. muciniphila\u003c/em\u003e, \u003cem\u003eC. porcorum\u003c/em\u003e, and \u003cem\u003eN. timonensis\u003c/em\u003e, and validated their roles in enhancing IBV resistance in chickens. Notably, administration of \u003cem\u003eA. muciniphila\u003c/em\u003e significantly improved chick survival to 90–95%, whereas other two bacterium exhibited relatively modest effects (Fig. 6B–C). Among microbial species, \u003cem\u003eA. muciniphila\u003c/em\u003e emerged as the most promising, given its well-documented benefits in gut health, aging mitigation, and metabolic regulation [53-58]. Recent studies have shown its antiviral potential against \u003cem\u003eBunyaviridae\u003c/em\u003e in mice [59], and our findings extend its therapeutic scope to gamma-coronavirus infections, demonstrating its potential for improving poultry disease resistance. Given the growing resistance to traditional antiviral drugs, probiotic-based strategies present a viable alternative, offering safety, cost-effectiveness, and immunomodulatory advantages [60, 61]. Our study lays the groundwork for leveraging \u003cem\u003eA. muciniphila\u003c/em\u003e and other beneficial bacteria in poultry farming and highlights the potential of multi-strain probiotic formulations to enhance therapeutic efficacy [62].\u003c/p\u003e\n\u003cp\u003eIBV infection induces severe inflammation in the kidneys and trachea, leading to high mortality in chickens [63]. Excessive inflammation is also a hallmark of coronavirus infections, including SARS-CoV-2, where cytokine storms drive severe outcomes [64]. Controlling inflammation is therefore a key strategy in mitigating disease severity. The gut microbiota plays a crucial role in regulating host inflammation and immunity via direct and indirect mechanisms [65-68]. For instance, extracellular vesicles from \u003cem\u003eRoseburia\u003c/em\u003e \u003cem\u003eintestinalis\u003c/em\u003e suppress inflammation and improve gut health [66]. Similarly, \u003cem\u003eA. muciniphila\u003c/em\u003e modulates host immunity through extracellular vesicles and other pathways [69, 70]. Previous studies have shown that its membrane phosphatidylethanolamine (PE) influences cytokine secretion and dendritic cell activation, fine-tuning immune responses [71]. Our study demonstrated that \u003cem\u003eA. muciniphila\u003c/em\u003e enhances IBV resistance by increasing GABA levels, which in turn suppress pro-inflammatory cytokines and alleviate kidney inflammation (Fig. 7G). GABA, a widely distributed non-proteinogenic amino acid, functions as a neurotransmitter and modulator of immune and metabolic processes [39, 72, 73]. Mechanistically, GABA inhibits IL-1β expression in macrophages by modulating NF-κB signaling and inflammasomes [39]. IBV-infected young chickens, NF-κB-mediated inflammation was excessively activated, but \u003cem\u003eA. muciniphila\u003c/em\u003e-induced GABA effectively suppressed this response.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study leveraged multi-omics approaches, integrating microbiomics and metabolomics, to explain the observed differential IBV resistance between young chickens and adult chickens. We identified \u003cem\u003eA. muciniphila\u003c/em\u003e as a key functional member of the gut microbiota responsible for IBV resistance. Mechanistically, we demonstrated that \u003cem\u003eA. muciniphila\u003c/em\u003e enhances resistance by upregulating GABA, thereby suppressing NF-κB-driven inflammation, mitigating nephritis, and strengthening antiviral immunity. These findings offer novel insights into microbiota-mediated antiviral defense and establish a scientific framework for advancing microbiome-targeted live biotherapeutic products (LBPs) as interventions against IBV and other emergent viral pathogens.\u003c/p\u003e\n\u003cp\u003eNevertheless, this study has certain limitations. First, our findings are based on yellow-feathered broilers, and further validation in other commercially important chicken lines, such as white-feathered broilers, is needed. Second, IBV is highly diverse, with at least eight circulating genotypes. Our study focused on the predominant QX-like genotype, and whether \u003cem\u003eA. muciniphila\u003c/em\u003e confers protection against other variants remains to be determined. Additionally, future studies should assess whether combining \u003cem\u003eA. muciniphila\u003c/em\u003e with \u003cem\u003eC. porcorum\u003c/em\u003e, \u003cem\u003eN. timonensis\u003c/em\u003e, or a multi-strain probiotic formulation could enhance IBV resistance. Addressing these gaps will help refine probiotic applications across different poultry populations and viral strains.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study provides novel insights into how \u003cem\u003eA. muciniphila\u003c/em\u003e confers IBV resistance via GABA-mediated immunomodulation, offering a new paradigm for microbiota-driven antiviral strategies in poultry. These findings pave the way for microbiome-based interventions. Future research should focus on refining probiotic applications, optimizing multi-strain formulations, and further elucidating microbiota-metabolite interactions in viral disease resistance. As antibiotic-free farming continues to gain traction, FMT-based biological control strategies hold immense promise for sustainable poultry health management and disease prevention.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYC, HZ and LD : conceived and designed the research. OP, YL, HZ and YC: designed experiments. OP, YL, XW, YH2, YY, QK, RG, GH, AL, FH, YX, XL, JL, YH2, ZK, YD and YZ: performed lab and animal experiments. CX, ZH and WL: provided provided resources. OP, YL, XW and LD: performed the data visualization. OP, XW, YH1 and LD: performed bioinformatics analysis. YC: provided funding support. OP, YL, LD and HZ: interpreted data. OP, YL, LD, HZ and YC: wrote and revised the manuscript. All authors provided insights, read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the State Key Laboratory of Biocontrol (Sun Yat-sen University) for supporting this work. This study was supported by National Key Research and Development Program, China (2021YFD1801101). We also sincerely thank Dr. Qingfeng Zhou, Dr. Lijuan Yin, Dr. Zhuanqiang Yan, and other colleagues in Research Institute of Wen\u0026rsquo;s Food Group for helping us to collect clinical samples and conduct the animal experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll sample collection and animal experiments in this study were conducted in accordance with the guidelines of the Animal Ethics Committees of Sun Yat-sen University and Harbin Veterinary Research Institute (HVRI) (2101112-01 and 230721-02-GR).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw sequencing data generated in this study, including 16S rRNA gene sequencing of 100 samples and metatranscriptomic sequencing of 60 samples, have been deposited in the National Center for Biotechnology Information (NCBI) database under the accession numbers PRJNA1219767 (https://dataview.ncbi.nlm.nih.gov/object/PRJNA1219767?reviewer=s96ihp5f67me7bt60gacd2ig6i) and PRJNA1222581 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1222581). The untargeted metabolomics data of 100 samples have been deposited in the MetaboLights database at the European Molecular Biology Laboratory (EMBL)\u0026ndash;European Bioinformatics Institute (EBI) under the accession number MTBLS12310 (https://www.ebi.ac.uk/metabolights/reviewer694c2e26-ed7a-464f-a12d-c0a70f0ded83).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDelzenne, Nathalie M., Laure B. Bindels, Audrey M. Neyrinck, Jens Walter. 2025. \u0026ldquo;The gut microbiome and dietary fibres: implications in obesity, cardiometabolic diseases and cancer.\u0026rdquo; \u003cem\u003eNature Reviews Microbiology\u003c/em\u003e 23: 225-238. https://doi.org/10.1038/s41579-024-01108-z\u003c/li\u003e\n\u003cli\u003ede Vos, Willem M., Herbert Tilg, Matthias Van Hul, Patrice D. 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However, the limited efficacy of commercial vaccines and the absence of approved antiviral drugs underscore the urgent need for novel strategies to combat IBV, particularly in the context of antibiotic-free poultry production. Emerging evidences suggest that gut microbiota plays a crucial role in shaping host immunity and antiviral resilience.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we demonstrated that gut microbiota composition is a key determinant of IBV resistance in chickens, as revealed through a fecal microbiota transplantation (FMT) model. Utilizing multi-omics approaches, we conducted comprehensive characterization of microbiomic, viromic, and metabolic differences among adult chickens, young chickens, and FMT-treated young chickens for the first time, establishing a mechanistic link between gut microbiota and IBV resistance. Building on these insights, longitudinal microbiome profiling in a newly developed persistent IBV infection model identified \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e as the key bacterium conferring IBV resistance, with its administration improving survival rates from 30–35% to 90–95%. Additionally, two novel candidate probiotics,\u003cem\u003eCloacibacillus porcorum\u003c/em\u003e and \u003cem\u003eNeglecta timonensis\u003c/em\u003e, exhibited moderate yet measurable protective effects. Mechanistic investigations revealed that \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e enhances γ-aminobutyric acid (GABA) production, which suppresses NF-κB-driven inflammatory responses, reduces pro-inflammatory cytokine levels, alleviates nephritis, and upregulates antiviral interferon expression, thereby fortifying host defenses against IBV infection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese findings provide a scientific foundation for deploying live biotherapeutic products (LBPs) as an intervention strategy against IBV and highlight the broader potential of gut microbiota modulation in mitigating infectious diseases and optimizing poultry health management.\u003c/p\u003e","manuscriptTitle":"Akkermansia muciniphila Enhances Resistance to Infectious Bronchitis Virus in Chickens Through γ-Aminobutyric Acid-Mediated Anti-Inflammatory Pathways","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 09:45:31","doi":"10.21203/rs.3.rs-6786224/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bdfe30b5-a78d-405e-97b3-82408eded45d","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-18T06:23:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-25 09:45:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6786224","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6786224","identity":"rs-6786224","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}