Abstract
Goose astrovirus (GAstV) is an important pathogen associated with visceral gout, growth retardation, and mortality in goslings, whereas the splenic response to infection remains poorly understood. In this study, GAstV-infected goslings showed positive viral RNA detection in spleen tissues, obvious splenomegaly, and histopathological lesions. Transcriptomic analysis identified 383 differentially expressed genes, including 243 upregulated and 140 downregulated genes. Functional enrichment analyses showed that these genes were mainly involved in antiviral defense, innate immune responses, complement and coagulation cascades, and the RIG-I-like receptor signaling pathway. GSEA further supported the enrichment of the RIG-I-like receptor signaling pathway in infected spleens. Notably, several genes related to this pathway, including DDX58, IFIH1, TRIM25, and IRF3, were markedly upregulated and were also identified as hub genes in the protein–protein interaction network. RT-qPCR validation further confirmed the upregulation of these hub genes in GAstV-infected spleens, corroborating the transcriptomic findings. Collectively, these findings indicate that GAstV infection is associated with splenic lesions and extensive transcriptional reprogramming in goslings, and suggest that RIG-I-like receptor-related antiviral signaling may be involved in the splenic innate immune response to GAstV infection. This study provides new transcriptomic insight into the innate immune responses associated with GAstV infection.
Keywords: goose astrovirus, innate immunity, RIG-I-like receptor signaling pathway, spleen, transcriptome
1. Introduction
Goose astrovirus (GAstV) is an emerging viral pathogen that has caused substantial economic losses in the goose industry in recent years (1, 2). Since its initial identification, GAstV infection has been associated with visceral gout, urate deposition, growth retardation, and increased mortality in goslings (3, 4). As a positive-sense single-stranded RNA virus, GAstV mainly affects young geese, spreads rapidly, and can cause severe disease, thereby posing a serious threat to waterfowl production (5, 6). Although the epidemiology, clinical manifestations, and genomic characteristics of GAstV have been increasingly investigated (7–9), the molecular basis of the host response to GAstV infection remains poorly understood.
The spleen is the largest peripheral immune organ in birds and plays a central role in immune surveillance, antigen processing, lymphocyte activation, and systemic immune regulation (10–12). During viral infection, the spleen often undergoes structural and functional changes and serves as an important site for host antiviral responses and immunopathological processes (13, 14). Previous studies have shown that GAstV infection can induce obvious splenic lesions, including lymphocyte degeneration, apoptosis or necrosis, reticular fiber disruption, and altered immune cell composition (15). In addition, GAstV infection activates innate immune signaling in the spleen, including pattern recognition receptors and downstream molecules such as RIG-I, MDA5, TLR3, MAVS, and IRF3, together with increased expression of interferon and antiviral proteins (16, 17). These findings suggest that the spleen is both a target organ of GAstV-induced injury and a key immune organ involved in antiviral defense. Notably, transcriptomic analysis of chicken astrovirus infection has revealed prominent interferon-induced antiviral signatures in the spleen, supporting the value of splenic transcriptome profiling for identifying key antiviral factors (18). However, the genome-wide transcriptional response and regulatory networks of the goose spleen after GAstV infection remain largely unknown. Therefore, spleen transcriptome analysis is needed to systematically identify the key immune-related genes and pathways involved in GAstV infection.
In this study, we established an experimental GAstV infection model in goslings and performed transcriptomic analysis of spleen tissue using RNA-seq. Differential expression, enrichment, and network analyses were subsequently conducted to investigate the transcriptomic changes induced by GAstV infection. We aimed to characterize the splenic transcriptional response to GAstV infection and identify key genes and pathways potentially involved in antiviral immunity, thereby facilitating further studies of goose antiviral immunity and GAstV pathogenesis.
2. Materials and methods
2.1. Animals and virus
The GAstV-2 isolate JSYC2503 used in this study was previously isolated and identified in our laboratory, and its complete genome sequence has been deposited in GenBank under accession no. PX548819.1. The strain was originally isolated from kidney, spleen, and liver tissues of a gosling with visceral gout in Yancheng, Jiangsu Province, China. Healthy 3-day-old Sanhua goslings were provided by Yangzhou Xiaopeng Agricultural Technology Co., Ltd. (Yangzhou, China).
2.2. Animal experiments
All goslings were reared under standard conditions with free access to feed and water. Before the experiment, all goslings were confirmed to be negative for GAstV by RT-PCR and showed no obvious clinical abnormalities. The goslings were randomly assigned to an infected group and a control group (n = 10 per group), with no significant difference in initial body weight between the groups. Following group assignment, the infected and control groups were housed in separate rooms under the same environmental and husbandry conditions throughout the experiment to minimize cross-transmission. The GAstV inoculum was prepared by diluting the filtered virus preparation in sterile phosphate-buffered saline (PBS), and its infectious titer was determined in goose embryo fibroblast (GEF) cells. Goslings in the infected group were inoculated intraperitoneally with 0.4 mL of the GAstV suspension at 106.68 TCID₅₀/mL, corresponding to approximately 1.9 × 106 TCID₅₀ per bird. Goslings in the control group received an equal volume of sterile PBS via the same route. The clinical status of the goslings was monitored daily throughout the experiment. At 10 dpi, all goslings were humanely euthanized by cervical dislocation, and spleen tissues were collected for gross lesion examination, viral detection, hematoxylin and eosin (H&E) staining, and transcriptome sequencing. Five spleen samples were randomly selected from each group for RNA-seq analysis. Some samples were immediately stored at −80 °C until further use.
2.3. RT-PCR detection of GAstV in spleen tissues
Total RNA was extracted from spleen tissues using FreeZol Reagent (Vazyme, R711, Nanjing, China) in accordance with the manufacturer’s instructions. Reverse transcription was performed using HiScript III Reverse Transcriptase (Vazyme, R302-01, Nanjing, China) to generate first-strand cDNA. Five spleen samples were randomly selected from each group for GAstV detection by conventional RT-PCR using ORF2-specific primers: GAstV-F (5′-GAAGGTGACCAAGAAGGTTAC-3′) and GAstV-R (5′-GAGTTGCTGTTTGCCCATTTTC-3′).
2.4. RNA sequencing
Total RNA extraction from spleen tissues, library construction, and sequencing were performed by Personal Biotechnology Co., Ltd. (Shanghai, China). Briefly, poly(A) + mRNA was enriched from total RNA using Oligo(dT) magnetic beads and fragmented to approximately 300 bp. First-strand cDNA was synthesized using random hexamer primers and reverse transcriptase, followed by second-strand cDNA synthesis. After PCR amplification, the libraries were size-selected, resulting in a final library size of approximately 450 bp. RNA quality was assessed using an Agilent 2,100 Bioanalyzer, and samples with an RNA integrity number (RIN) ≥ 8 were used for library construction. Libraries that passed quality control were sequenced on the Illumina NovaSeq 6,000 platform to generate 150-bp paired-end reads (PE150).
2.5. RNA-seq data processing and quality assessment
Raw reads were quality-filtered by removing adapter sequences, reads containing excessive ambiguous bases, and low-quality reads to obtain clean reads for downstream analysis. Sequencing quality metrics, including Q20, Q30, and GC content, were calculated for each sample. Clean reads were then aligned to the goose reference genome (Anser cygnoides; Ensembl, GooseV1.0) using HISAT2 (v2.1.0). The total mapping rate and the proportions of uniquely mapped, multiple mapped, and unmapped reads were calculated to evaluate sequencing quality and alignment performance. Based on the gene annotation file, read counts for each gene were generated using HTSeq (v0.9.1). To further assess the reliability and comparability of the RNA-seq data, count data were transformed using the variance stabilizing transformation (VST) function in the DESeq2 package (v1.46.0). The VST-transformed expression values were used only for visualization analyses, including boxplots, sample correlation analysis, and partial least squares discriminant analysis (PLS-DA). These analyses were performed to assess the consistency of expression distributions, within-group reproducibility, and overall differences in expression patterns between groups.
2.6. Differential expression analysis
Differential expression analysis was performed using the DESeq2 package in R based on raw read counts from spleen samples collected at 10 dpi from the GAstV-infected and control groups. Multiple testing correction was performed using the Benjamini–Hochberg method. Genes with an adjusted p value < 0.05 and an absolute log2 fold change > 1 were defined as differentially expressed genes (DEGs).
2.7. GO and KEGG enrichment analyses
Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the clusterProfiler package in R (v4.14.6). Because goose gene annotation is limited, goose genes were first mapped to their corresponding human orthologs using a three-tier strategy, and enrichment analysis was subsequently conducted based on the mapped gene set. We first obtained explicit human orthologs from NCBI Gene Orthologs through Ensembl Genes release 116 cross-references for GooseV1.0 (GCA_002166845.1) and human GRCh38. We then compared genes without an NCBI mapping bidirectionally using Ensembl release 116 protein sequences and DIAMOND v2.2.4 in very-sensitive mode. We retained only unique reciprocal best hits with an E-value ≤ 1 × 10−5, amino-acid identity ≥ 30%, and query and subject coverage ≥ 50%. We mapped the remaining genes by case-insensitive exact matching of Swiss-Prot annotation names to unique human Ensembl gene symbols. We excluded ambiguous one-to-many matches and collapsed repeated human targets from many-to-one mappings before enrichment analysis. This procedure mapped 13,969 of the 14,774 tested genes (94.55%) and 360 of the 383 DEGs (93.99%) to human gene identifiers. The GO and KEGG analyses used the complete human annotation databases as the default backgrounds, comprising 18,723 GO-annotated genes and 9,396 KEGG-annotated genes, respectively. This standardized reference allowed the query and background genes to be evaluated within the same human annotation space and reduced reliance on incomplete goose-specific annotations. p values were adjusted using the Benjamini–Hochberg method, and an adjusted p value < 0.05 was considered statistically significant.
2.8. Gene set enrichment analysis
Gene set enrichment analysis (GSEA) was performed using the clusterProfiler package in R (v4.14.6). Because the gene sets in the Molecular Signatures Database (MSigDB, v2024.1) are mainly based on human gene annotations, goose genes were first mapped to their corresponding human orthologs. All mapped genes were ranked in descending order based on their log₂ fold change values, and GSEA was performed using KEGG pathway gene sets obtained from MSigDB. p values were adjusted using the Benjamini–Hochberg method, and a false discovery rate (FDR) < 0.05 was used as the threshold for significant enrichment.
2.9. Protein–protein interaction network analysis
Antiviral response- and RIG-I-like receptor signaling-related differentially expressed genes were selected based on the functional enrichment results, mapped to their corresponding human orthologs, and submitted to the STRING database (v12.0) for construction of a protein–protein interaction (PPI) network. Because this analysis was intended for exploratory module detection and candidate-gene prioritization, interactions with a combined score > 0.4 (medium confidence) were retained to balance interaction confidence and network coverage. The resulting network was visualized using Cytoscape (v3.10.4). Candidate hub genes were prioritized using the cytoHubba plugin based on the maximal clique centrality (MCC) algorithm, and network modules were detected using the MCODE plugin. The expression levels of candidate hub genes were represented by FPKM values derived from the RNA-sequencing data, and representative antiviral genes were selected for subsequent RT-qPCR validation of their expression patterns. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as an internal control, and the primer sequences are shown in Table 1.
Table 1.
Primer sequences for RT-qPCR.
| Name | Sequences (5′ → 3′) | GenBank No. |
|---|---|---|
| DDX58 | F: AGGAGAGCAGGATATGTAGA R: TCGGTCAGGTAGGATAGAG |
XM_048051753.2 |
| IFIH1 | F: AATCTGGATAGTGGTTCTGTC R: TATCTTCTGTAGCGGCATCT |
XM_013171142.3 |
| DHX58 | F: GCTGGTGATAGACGAGTGCC R: TGCGCTGGAGGTAATTCAACA |
XM_048053209.2 |
| TRIM25 | F: GCTCATAACAAAGTGCTTCT R: CAGTAGTGGATGCCTCTC |
XM_048064253.2 |
| RSAD2 | F: AGAGCCTTCCAACTCTATCCT R: ACTCCACACATACTTTCCTCC |
XM_013172803.3 |
| OASL | F: GGACATCAAGAACCAGACGAT R: GTGCAGAAGCAGGAAGATAGT |
XM_048074148.2 |
| GAPDH | F: GCCGAATATGTTGTGGAGTC R: GGAGATGATGACACGCTTAG |
XM_067004670.1 |
2.10. RT-qPCR validation
To evaluate the reproducibility of the RNA-seq expression patterns in non-overlapping biological samples, three spleen samples per group that were not included in the RNA-seq analysis were used for RT-qPCR validation. Total RNA was extracted using FreeZol Reagent (R711-02, Vazyme Biotech Co., Ltd., Nanjing, China), and first-strand cDNA was synthesized using HiScript III Reverse Transcriptase (R312-02, Vazyme Biotech Co., Ltd., Nanjing, China) according to the manufacturer’s instructions. RT-qPCR was performed using ChamQ Blue Universal SYBR qPCR Master Mix (Q312-02, Vazyme Biotech Co., Ltd., Nanjing, China) on a CFX Connect Real-Time PCR Detection System (Bio-Rad Laboratories, Inc., Hercules, CA, USA). The thermal cycling program consisted of an initial denaturation at 95 °C for 30 s, followed by 40 cycles at 95 °C for 10 s and 60 °C for 30 s, with a subsequent melting-curve analysis. Each biological sample was analyzed in technical triplicate. GAPDH was used as the reference gene, and relative gene expression was calculated using the 2−ΔΔCt method, with the control group used as the calibrator. Primer sequences are listed in Table 1.
2.11. Statistical analysis
All statistical analyses and data visualization were performed using GraphPad Prism 9.5, except for RNA-seq differential expression analysis, enrichment analysis, and PPI network analysis. Data are presented as mean ± standard deviation (SD). Spleen weights and RT-qPCR data were compared between the control and GAstV groups using two-tailed unpaired Student’s t-tests. FPKM expression levels were compared using two-tailed unpaired Mann–Whitney U tests. A p value < 0.05, as appropriate, was considered statistically significant.
3. Results
3.1. Clinical status of goslings and characteristics of spleen samples after GAstV infection
To clarify the experimental design and analytical strategy used to evaluate spleen involvement following GAstV infection, the overall workflow of this study is presented in Figure 1A. At 10 days post-infection, RT-PCR detection showed a GAstV-specific band in spleen tissues from the infected group, whereas no positive signal was observed in the control group, confirming viral presence in the spleen after GAstV infection (Figure 1B). Gross examination revealed that spleens from GAstV-infected goslings were visibly enlarged compared with those from control goslings (Figure 1C). Consistently, spleen weight was significantly increased in the infected group (Figure 1D), indicating marked splenomegaly after infection. H&E staining further showed evident splenic lesions in the infected group, including disorganized tissue architecture, congestion, cellular swelling, vacuolar degeneration, and inflammatory cell infiltration, whereas the control spleens showed relatively normal histological structure (Figure 1E). Together, these findings confirmed successful GAstV infection in the challenged goslings and demonstrated obvious spleen involvement and pathological injury, supporting the use of these spleen samples for subsequent transcriptomic analysis.
Figure 1.
Experimental workflow and characterization of spleen samples after GAstV infection: (A) Schematic overview of GAstV infection and subsequent analyses. (B) RT-PCR detection of GAstV in spleen tissues. (C) Gross morphology of spleens from control and GAstV-infected goslings. (D) Spleen weight comparison between control and GAstV-infected groups. (E) Representative H&E-stained spleen sections from control and GAstV-infected goslings. Spleen weight data in panel D are presented as mean ± SD (n = 5 biological replicates per group). **p < 0.01.
3.2. Quality assessment of RNA-seq data
To assess RNA-seq data quality, all spleen samples underwent quality control. All samples yielded sufficient clean reads, with high Q20 and Q30 values and GC contents within the expected range (Supplementary Table S1). After alignment to the goose reference genome, all samples showed high overall mapping rates. Uniquely mapped reads accounted for most alignments, whereas the proportions of multi-mapped and unmapped reads were low (Figure 2A). These metrics indicated satisfactory sequencing and alignment quality. Boxplots of VST-transformed expression values showed comparable distributions across samples, supporting consistent normalization among libraries (Figure 2B). Sample-to-sample correlation analysis showed tight clustering of control samples and greater variability among GAstV-infected samples (Figure 2C). Partial least squares discriminant analysis (PLS-DA) further showed group-level separation (Supplementary Table S2). Components 1 and 2 explained 23.67% and 18.99% of the X variance, respectively (Figure 2D). Leave-one-out cross-validation yielded 90% classification accuracy under both distance rules. Exact permutation testing across all 252 balanced label assignments showed that the observed accuracy exceeded chance (max-distance, p = 0.0317; centroid-distance, p = 0.0476). Nevertheless, the GAstV-infected samples remained more broadly distributed than the control samples (Figure 2D). Collectively, these analyses confirmed the reliability of the RNA-seq data and revealed distinct splenic transcriptomic profiles between control and GAstV-infected goslings.
Figure 2.
Quality assessment and sample distribution of RNA-seq data from spleens of control and GAstV-infected goslings. (A) Read mapping statistics for each sample after alignment to the goose reference genome. (B) Boxplot of VST-normalized gene expression values across samples. (C) Sample-to-sample correlation heatmap. (D) Partial least squares discriminant analysis (PLS-DA) score plot of RNA-seq samples.
3.3. Identification of differentially expressed genes in the spleen following GAstV infection
Differential expression analysis was performed to identify transcriptional changes in the spleen following GAstV infection. A total of 383 differentially expressed genes (DEGs) were identified between the infected and control groups, including 243 upregulated and 140 downregulated genes (Supplementary Tables S3, S4). The volcano plot showed the overall distribution of DEGs, with several antiviral and immune-related genes, such as IFIH1 (log2FC = 1.48, adjusted p = 3.70 × 10−4), DDX58 (log2FC = 2.05, adjusted p = 1.54 × 10−5), DHX58 (log2FC = 2.42, adjusted p = 8.09 × 10−6), IRF3 (log2FC = 1.89, adjusted p = 1.76 × 10−3), TRIM25 (log2FC = 1.40, adjusted p = 1.45 × 10−5), STAT2 (log2FC = 1.55, adjusted p = 3.75 × 10−3), EIF2AK2 (log2FC = 1.82, adjusted p = 2.56 × 10−4), RSAD2 (log2FC = 3.40, adjusted p = 0.0170), OASL (log2FC = 3.79, adjusted p = 6.46 × 10−4), IFIT5 (log2FC = 3.05, adjusted p = 1.07 × 10−2), IFITM3 (log2FC = 5.04, adjusted p = 4.26 × 10−4), USP18 (log2FC = 3.53, adjusted p = 5.42 × 10−5), MX2 (log2FC = 3.92, adjusted p = 3.25 × 10−6) and CXCL8 (log2FC = 1.11, adjusted p = 2.93 × 10−2), prominently upregulated in the infected group (Figure 3A). In contrast, representative downregulated genes included KIT (log2FC = −1.57, adjusted p = 7.17 × 10−5), TREM2 (log2FC = −2.23, adjusted p = 1.04 × 10−2), and CCR4 (log2FC = −1.19, adjusted p = 3.14 × 10−2). The functional annotations of these genes included immune-cell differentiation, myeloid-cell activation and phagocytosis, and chemotactic responses, respectively. Hierarchical clustering analysis based on all DEGs clearly separated GAstV-infected samples from control samples, indicating distinct splenic transcriptomic profiles between the two groups (Figure 3B).
Figure 3.
Identification of differentially expressed genes in the spleens of control and GAstV-infected goslings: (A) Volcano plot showing the distribution of differentially expressed genes between the control and GAstV-infected groups. (B) Hierarchical clustering heatmap of differentially expressed genes in spleen samples.
3.4. GO enrichment analysis of differentially expressed genes
GO enrichment analysis showed that these DEGs were mainly enriched in biological processes associated with antiviral response, interferon production, and innate immunity (Supplementary Table S5). Among these, defense response to virus and response to virus were the most significantly enriched terms, along with terms related to regulation of viral process, MDA5 signaling pathway, and interferon production (Figure 4A). The cnetplot analysis showed that IFIH1, DHX58, DDX58, IRF3, STAT2, IFIT5, IFITM3, GBP1, GBP3, and RSAD2 were shared across multiple antiviral-related enriched terms, indicating that these genes were broadly associated with virus recognition and interferon-related processes (Figure 4B).
Figure 4.
GO biological process enrichment analysis of differentially expressed genes in the spleens of control and GAstV-infected goslings (A) Bubble plot of significantly enriched biological process terms. (B) Gene-concept network plot showing the relationships between differentially expressed genes and enriched biological process terms.
3.5. KEGG pathway enrichment analysis of differentially expressed genes
KEGG enrichment analysis further showed that splenic DEGs following GAstV infection were mainly involved in multiple immune-related pathways (Supplementary Table S6). Specifically, the DEGs were significantly enriched in the complement and coagulation cascades and the RIG-I-like receptor signaling pathway. In addition, the NOD-like receptor signaling pathway and the PI3K–Akt signaling pathway were identified among the enriched pathways (Figure 5). Among these pathways, the RIG-I-like receptor signaling pathway was one of the most prominent antiviral pathways, and was therefore selected for further analysis.
Figure 5.
KEGG pathway enrichment analysis of differentially expressed genes.
3.6. GSEA enrichment analysis
To further evaluate whether immune-related pathways showed coordinated changes at the whole-transcriptome level, GSEA was performed using the ranked gene list of all detected genes. KEGG-based GSEA identified several pathways with coordinated enrichment patterns between the GAstV-infected and control groups (Supplementary Table S7), including JAK–STAT signaling pathway, lysosome, proteasome, ribosome, RIG-I-like receptor signaling pathway, and Toll-like receptor signaling pathway (Figure 6). Among these pathways, the RIG-I-like receptor signaling pathway showed a positive enrichment tendency toward the GAstV-infected group. These results were consistent with the DEG-based KEGG enrichment analysis and suggested that RIG-I-like receptor-mediated viral RNA sensing may contribute to the splenic antiviral response following GAstV infection.
Figure 6.
Gene set enrichment analysis of KEGG pathways in spleen transcriptomes from control and GAstV-infected goslings.
3.7. Identification of hub genes associated with RIG-I-like receptor-mediated antiviral responses
To identify key genes involved in the antiviral response to GAstV infection, a PPI network was constructed using immune- and antiviral-related DEGs (Figure 7A). The MCC algorithm identified IFIH1, DDX58, IRF3, TRIM25, EIF2AK2, STAT2, DHX58, OASL, GBP3, and RSAD2 as the top 10 hub genes (Figure 7B). In addition, MCODE analysis extracted a highly connected module consisting of IFIH1, DDX58, IRF3, and TRIM25 (Figure 7C), suggesting that these genes may represent a core antiviral regulatory cluster.
Figure 7.
PPI network analysis of selected antiviral response-related differentially expressed genes in the spleens of control and GAstV-infected goslings. (A) PPI network of selected antiviral response-related differentially expressed genes. (B) Visualization of the top 10 hub genes identified by cytoHubba. (C) Key module identified from the PPI network by MCODE analysis. (D) Heatmap of selected hub genes. (E) FPKM values of selected hub genes in control and GAstV-infected goslings. (F) Relative expression levels of selected antiviral genes in control and GAstV-infected goslings. (A–E) were based on RNA-seq data from five biological samples per group. Panel F included three independent biological samples per group that were not used for RNA-seq, with each sample analyzed in technical triplicate. Data in panels E and F are presented as mean ± SD. *p < 0.05, **p < 0.01, and ***p < 0.001.
The heatmap showed that most hub genes were upregulated in GAstV-infected spleens compared with controls (Figure 7D). Consistently, FPKM analysis revealed significantly increased expression of several RIG-I-like receptor signaling-related genes, including DDX58, IFIH1, DHX58, TRIM25, RSAD2 and OASL, in the infected group (Figure 7E). To further validate the transcriptomic results, RT-qPCR was performed for selected antiviral genes. The expression levels of DDX58, IFIH1, DHX58, TRIM25, RSAD2, and OASL were all significantly elevated after GAstV infection (Figure 7F). These results indicate that GAstV infection activates a RIG-I-like receptor-associated antiviral response in the spleen, with DDX58, IFIH1, IRF3, and TRIM25 potentially serving as key regulatory nodes.
4. Discussion
Previous studies on GAstV have mainly focused on gout, renal injury, and metabolic disorders (19–23), whereas the splenic immune response to GAstV infection remains relatively less characterized compared with renal lesions and gout-associated pathology (8). In the present study, infected goslings showed obvious splenomegaly, positive viral detection in the spleen, and histopathological lesions, including tissue disorganization, congestion, vacuolar degeneration, and inflammatory cell infiltration. These findings indicate that the spleen is involved in GAstV infection and may participate actively in the host response rather than being only a secondary target organ.
KEGG analysis identified the complement and coagulation cascades as a top-ranked pathway, providing a possible link between the splenic response and the systemic lesions of GAstV infection. Eight DEGs contributed to this enrichment. The upregulated genes included C1QB (log2FC = 1.65, adjusted p = 5.13 × 10−3), C1S (log2FC = 1.32, adjusted p = 2.60 × 10−2), and C3AR1 (log2FC = 1.23, adjusted p = 2.26 × 10−4). C5AR1 (log2FC = 1.36, adjusted p = 3.89 × 10−2) and SERPING1 (log2FC = 3.10, adjusted p = 1.26 × 10−14) were also upregulated. In contrast, F13B (log2FC = −1.47, adjusted p = 2.23 × 10−2), BDKRB2 (log2FC = −2.07, adjusted p = 2.24 × 10−2), and CFHR1 (log2FC = −1.34, adjusted p = 4.79 × 10−2) were downregulated. Monosodium urate crystals can activate complement and generate C3a and C5a, with C5a promoting IL-1 production and leukocyte recruitment (24). The increased expression of C3AR1 and C5AR1 may therefore reflect altered responsiveness to complement-derived inflammatory signals associated with urate deposition. Changes in F13B and BDKRB2 also point to altered coagulation and vascular regulation, which may be relevant to the splenic congestion observed here. GAstV infection can also cause hepatic sinus narrowing (22), together with renal tubular injury and urate deposition (19, 23). However, the concurrent upregulation of SERPING1 and downregulation of CFHR1 indicate that the pathway did not change uniformly. These results should therefore be interpreted as transcriptional perturbation of complement- and coagulation-related processes rather than direct evidence of complement activation or coagulopathy. Because the data were obtained from spleen tissue at 10 dpi, it remains unclear whether these changes contribute to urate deposition and renal injury or occur secondarily to systemic inflammation. Further measurements of complement activation products, coagulation parameters, and renal tissue changes are needed to clarify this relationship.
The spleen is a major immune organ involved in systemic immune regulation, and transcriptomic studies in chickens have shown that splenic gene expression changes can reflect immune functional alterations (10, 25). As GAstV is an RNA virus, one of the earliest events after infection is likely the recognition of viral RNA by host pattern-recognition receptors (26, 27). Previous studies have also shown that GAstV or goose-origin nephrotic astrovirus infection can activate host innate immune responses (28). Our data suggest that this process may be mainly associated with the RIG-I-like receptor pathway. In particular, DDX58, IFIH1, and DHX58 were all markedly upregulated in infected spleens. DDX58 encodes RIG-I and IFIH1 encodes MDA5, both of which are central cytoplasmic sensors of viral RNA, whereas DHX58 acts as an important regulator of RIG-I and MDA5-mediated signaling (27). The concurrent increase in these genes suggests that GAstV infection may activate a coordinated viral RNA-sensing response in the spleen.
Downstream molecules in this pathway were also induced. TRIM25, which is important for RIG-I activation (29, 30), and IRF3, a key transcription factor involved in interferon induction (26, 27, 31), were both identified as central antiviral genes in the infected spleen. These findings suggest the possible involvement of a RIG-I/MDA5-associated antiviral signaling cascade in splenic tissue. Previous studies reported similar induction of antiviral genes during GAstV infection in other systems (29, 32). Our results extend these observations to the goose spleen and suggest that viral RNA sensing through RIG-I-like receptors may be an important component of the splenic innate immune response to GAstV.
Another notable feature of this response was the upregulation of STAT2 and multiple interferon-stimulated antiviral genes. These genes are involved in several stages of antiviral defense, such as viral RNA recognition, inhibition of translation, restriction of viral replication, and regulation of interferon signaling (33, 34). Their coordinated induction suggests that GAstV infection may establish an interferon-associated antiviral state in the spleen. Such a response is likely beneficial for limiting viral spread, but it may also reflect strong immune activation within splenic tissue.
This point is important because the pathological changes observed in the spleen suggest that antiviral defense and tissue injury may occur simultaneously. Inflammatory cell infiltration and congestion were evident in infected spleens, and CXCL8 was also among the upregulated genes. This suggests that GAstV infection may promote leukocyte recruitment and local inflammatory responses in the spleen (35). Therefore, the splenic lesions observed in infected goslings may not be caused only by direct viral injury, but may also be associated with excessive innate immune activation (36, 37). In this context, activation of the RIG-I-like receptor pathway may help restrict GAstV infection while also contributing to inflammatory damage when strongly activated (37).
Previous studies have shown that in the canonical RIG-I-like receptor pathway, viral RNA recognition by RIG-I and MDA5 is followed by signal transduction through MAVS and downstream kinase complexes, including TBK1 and IKK (38). This signaling cascade activates transcription factors such as IRF3 and NF-κB, thereby promoting type I interferon production and inflammatory cytokine expression (39–41). Secreted type I IFNs can subsequently activate IFNAR-mediated JAK–STAT signaling and induce a broad set of interferon-stimulated genes (42–44). In the present study, the upregulation of DDX58, IFIH1, DHX58, TRIM25, IRF3, STAT2, and multiple ISGs is consistent with this previously described antiviral signaling framework. Nevertheless, although MAVS, TBK1, IKK, and NF-κB are well-established components of this pathway, their activation in GAstV-infected goose spleens was not directly examined at the protein or phosphorylation level in the present study. Therefore, the involvement of these canonical signaling intermediates in GAstV-infected goose spleens should be interpreted cautiously and requires further experimental validation in this infection context.
To integrate these transcriptomic and histopathological findings, we propose a working model for the splenic innate immune response to GAstV infection (Figure 8). In this model, GAstV RNA may be recognized by RIG-I-like receptors, as supported by the upregulation of DDX58, IFIH1, and DHX58 in infected spleens. TRIM25, which facilitates RIG-I activation, and IRF3, a key transcription factor involved in interferon induction, were also increased, suggesting that GAstV infection may activate an antiviral signaling cascade associated with RIG-I and MDA5. Consistent with this model, the marked induction of interferon-stimulated genes further indicates the establishment of an interferon-associated antiviral state in the spleen. In parallel, the upregulation of CXCL8 and the presence of inflammatory cell infiltration suggest that this antiviral response may be accompanied by chemokine-mediated inflammatory activation. Therefore, the proposed model links GAstV-induced viral RNA sensing with both antiviral defense and inflammatory splenic injury.
Figure 8.
Transcriptome-supported working model of GAstV-associated innate immune responses in gosling spleens at 10 dpi. Transcriptomic data, supported by RT-qPCR validation of selected genes, revealed increased expression of viral RNA-sensing-related genes and multiple interferon-stimulated genes, suggesting the possible involvement of RIG-I-like receptor-related antiviral responses. MAVS/TBK1/IKK signaling, IRF3 and NF-κB activation, type I IFN production, and JAK–STAT signaling are depicted as inferred canonical events that were not directly validated. The temporal and causal relationships among viral sensing, ISG induction, inflammation, and splenic injury remain unresolved.
Previous studies have reported that astrovirus infection can cause splenic lymphocyte depletion, apoptosis, and immunosuppression in goslings (15, 45). Several genes associated with immune-cell development and function, including KIT, TREM2, and CCR4, were downregulated in infected spleens. Their reduced expression may be consistent with the previously reported splenic lymphocyte depletion and immunosuppression. These findings complement previous reports by showing that infected spleens also mounted an antiviral transcriptional response. At 10 dpi, antiviral activation coexisted with reduced expression of immune-cell-associated genes. Whether these concurrent transcriptional changes contribute to splenic injury and immune dysfunction requires further investigation.
Several limitations should be acknowledged. First, spleen tissues were collected only at 10 dpi; therefore, the present study provides a single-time-point transcriptional snapshot and cannot distinguish early viral RNA-sensing events from sustained antiviral responses or secondary transcriptional changes associated with established inflammation and tissue injury. Second, intraperitoneal inoculation bypasses the predominantly fecal–oral route of GAstV transmission and may elicit splenic responses that differ in magnitude or kinetics from those occurring during natural infection. Third, although RNA-seq and RT-qPCR revealed increased expression of DDX58, IFIH1, DHX58, TRIM25, IRF3, STAT2, and multiple ISGs, these findings provide pathway-level evidence rather than direct functional validation of RIG-I-like receptor signaling. The specific roles of RIG-I and MDA5 in GAstV recognition, as well as downstream events involving MAVS, TBK1, IKK, and IRF3, require further validation at the protein and phosphorylation levels. Fourth, the STRING network topology, MCC-based hub-gene ranking, and MCODE-defined modules may vary with the selected confidence threshold. The identified genes and modules should therefore be regarded as network-prioritized candidates, and the predicted protein interactions require experimental validation. Fifth, because the GO and KEGG analyses relied on mapped human orthologs and complete human annotation backgrounds, conserved and well-annotated genes may be preferentially represented, whereas goose-specific or poorly annotated responses may be underrepresented. Finally, the splenic cell populations responsible for these responses remain unknown. Future studies incorporating infection models that more closely mimic natural exposure, multiple post-infection time points, protein-level and functional validation, quantitative histopathological scoring, and cell-type-specific analyses will be needed to define the temporal and mechanistic relationships among viral sensing, antiviral responses, and splenic injury. Despite these limitations, our findings provide a transcriptomic framework and identify candidate pathways and genes for further investigation of GAstV pathogenesis in goslings.
5. Conclusion
GAstV infection caused obvious splenic lesions and markedly altered the splenic transcriptional profile of goslings. The host response was characterized by enhanced antiviral innate immune signaling, particularly enrichment of the RIG-I-like receptor pathway and upregulation of related genes, including DDX58, IFIH1, DHX58, TRIM25, and IRF3. These findings suggest that RIG-I-like receptor-mediated viral RNA sensing is likely involved in the splenic response to GAstV infection and provide new insight into host innate immune responses associated with GAstV infection.
Funding Statement
The author(s) declared that financial support was received for this work and/or its publication. This research was funded by the Earmarked Fund for Modern Agro-industry Technology Research System in Waterfowl Industry (CARS-42-3).
Footnotes
Edited by: Jesús Hernández, National Council of Science and Technology (CONACYT), Mexico
Reviewed by: Md. Aminul Islam, Harvard University, United States
Jake J. Wen, University of Texas Medical Branch at Galveston, United States
Sandeep Saini, Goswami Ganesh Dutta S.D. College, India
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: PRJNA1479086 (NCBI-BioProject-SRA).
Ethics statement
The animal study was approved by the Committee on the Ethics of Animal Experiments of Yangzhou University (approval no. 202509006; approved on 2 September 2025), Yangzhou, China. The study was conducted in accordance with local legislation and institutional requirements.
Author contributions
GP: Writing – original draft, Methodology, Conceptualization. XG: Conceptualization, Methodology, Writing – review & editing. YG: Methodology, Formal analysis, Writing – review & editing, Validation. SF: Methodology, Writing – review & editing, Investigation, Validation. XH: Validation, Writing – review & editing, Conceptualization, Data curation. YZ: Software, Visualization, Writing – review & editing. GC: Project administration, Supervision, Conceptualization, Writing – review & editing. WZ: Resources, Conceptualization, Writing – review & editing, Project administration, Supervision, Funding acquisition. QX: Conceptualization, Resources, Funding acquisition, Supervision, Writing – review & editing, Project administration.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that Generative AI was not used in the creation of this manuscript.
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Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fvets.2026.1911277/full#supplementary-material
Summary of RNA-seq sequencing quality metrics for each spleen sample.
PLS-DA explained variance, cross-validation, and permutation-test results.
Expression profiles and annotations of all expressed genes.
Differentially expressed genes between GAstV-infected and control gosling spleens.
GO enrichment analysis of differentially expressed genes.
KEGG pathway enrichment analysis of differentially expressed genes.
GSEA results based on ranked expressed genes.
References
- 1.Li H, Li N, Wang Z, Zou Y, Jiang H, Xiong L. Research note: the spatiotemporal patterns of goose astrovirus in China. Poult Sci. (2025) 104:105780. doi: 10.1016/j.psj.2025.105780, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Zhu Q, Sun D. Goose Astrovirus in China: a comprehensive review. Viruses. (2022) 14:1759. doi: 10.3390/v14081759, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Fu X, Hou Z, Liu W, Cao N, Liang Y, Li B, et al. Insight into the epidemiology and evolutionary history of novel goose Astrovirus-associated gout in goslings in southern China. Viruses. (2022) 14:1306. doi: 10.3390/v14061306, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Xiang Y, Chen M, Sun M, Dong J, Zhang J, Huang Y, et al. Isolation, identification, and epidemiological characteristics of goose astrovirus causing acute gout in Guangdong province, China. Poult Sci. (2024) 103:104143. doi: 10.1016/j.psj.2024.104143, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Ren Z, Zhang Q, Li J, Yu Z, Fu G, Liu R, et al. Detection and characterization of goose Astrovirus infections in hatcheries and commercial goose flocks. Transbound Emerg Dis. (2023) 2023:1127544. doi: 10.1155/2023/1127544, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Zhang X, Deng T, Song Y, Liu J, Jiang Z, Peng Z, et al. Identification and genomic characterization of emerging goose astrovirus in Central China, 2020. Transbound Emerg Dis. (2022) 69:1046–55. doi: 10.1111/tbed.14060, [DOI] [PubMed] [Google Scholar]
- 7.Fei Z, Jiao A, Xu M, Wu J, Wang Y, Yu J, et al. Genetic diversity and evolution of goose astrovirus in the east of China. Transbound Emerg Dis. (2022) 69:e2059–72. doi: 10.1111/tbed.14542, [DOI] [PubMed] [Google Scholar]
- 8.Ren D, Zhang H, Ye X, Jia X, Chen R, Tang T, et al. Current situation of goose Astrovirus in China: a review. Viruses. (2025) 17:84. doi: 10.3390/v17010084, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Wang H, Zhu Y, Ye W, Hua J, Chen L, Ni Z, et al. Genomic and epidemiological characteristics provide insights into the phylogeographic spread of goose astrovirus in China. Transbound Emerg Dis. (2022) 69:e1865–76. doi: 10.1111/tbed.14522, [DOI] [PubMed] [Google Scholar]
- 10.Lu M, Lee Y, Lillehoj HS. Evolution of developmental and comparative immunology in poultry: the regulators and the regulated. Dev Comp Immunol. (2023) 138:104525. doi: 10.1016/j.dci.2022.104525, [DOI] [PubMed] [Google Scholar]
- 11.Tang LP, Liu YL, Ding KN, Hou XJ, Qin JJ, Zhang YA, et al. Chai Hu oral liquid enhances the immune functions of both spleen and bursa of Fabricius in heat-stressed broilers through strengthening TLR4-TBK1 signaling pathway. Poult Sci. (2021) 100:101302. doi: 10.1016/j.psj.2021.101302, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Zhou Y, Cao D, Liu J, Li F, Han H, Lei Q, et al. Chicken adaptive response to nutrient density: immune function change revealed by transcriptomic analysis of spleen. Front Immunol. (2023) 14:1188940. doi: 10.3389/fimmu.2023.1188940, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Rabiei M, Low WY, Ren Y, Cahyono MI, Doan PTK, Dharmayanti I, et al. Indicators of the molecular pathogenesis of virulent Newcastle disease virus in chickens revealed by transcriptomic profiling of spleen. Sci Rep. (2021) 11:17570. doi: 10.1038/s41598-021-96929-w, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Wang S, Huang T, Xie Z, Wan L, Ren H, Wu T, et al. Transcriptomic and Translatomic analyses reveal insights into the signaling pathways of the innate immune response in the spleens of SPF chickens infected with avian Reovirus. Viruses. (2023) 15:2346. doi: 10.3390/v15122346, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Ding R, Huang H, Wang H, Yi Z, Qiu S, Lv Y, et al. Goose nephritic Astrovirus infection of goslings induces lymphocyte apoptosis, reticular Fiber destruction, and CD8 T-cell depletion in spleen tissue. Viruses. (2021) 13:1108. doi: 10.3390/v13061108, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Wu W, Qiu S, Huang H, Xu R, Bao E, Lv Y. Immune-related gene expression in the kidneys and spleens of goslings infected with goose nephritic astrovirus. Poult Sci. (2021) 100:100990. doi: 10.1016/j.psj.2021.01.013, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Xu L, Jiang B, Cheng Y, He Y, Wu Z, Wang M, et al. Infection and innate immune mechanism of goose astrovirus. Front Microbiol. (2023) 14:1121763. doi: 10.3389/fmicb.2023.1121763, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Sajewicz-Krukowska J. Transcriptome sequencing of the spleen reveals antiviral response genes in chickens infected with CAstV. Viruses. (2021) 13:2374. doi: 10.3390/v13122374, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Bi Z, Lv X, Zhang Z, Cai L, Zhang M, Li W, et al. Emerging fatal gout disease in Chinese goslings linked to acute kidney injury induced by novel goose astrovirus infection. Front Cell Infect Microbiol. (2024) 14:1470808. doi: 10.3389/fcimb.2024.1470808, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Fan S, Hu X, Chai W, Shan X, Gu Y, Shen H, et al. AGXT-driven bile acid dysregulation triggers viral gout in Astrovirus-infected Jiangnan white geese. Vet Sci. (2025) 12:951. doi: 10.3390/vetsci12100951, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Liu C, Sun M, Liao M. A review of emerging goose Astrovirus causing gout. Biomed Res Int. (2022) 2022:1635373. doi: 10.1155/2022/1635373, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Wang A, Xie J, Wu Z, Liu L, Wu S, Feng Q, et al. Pathogenicity of a goose astrovirus 2 strain causing fatal gout in goslings. Microb Pathog. (2023) 184:106341. doi: 10.1016/j.micpath.2023.106341, [DOI] [PubMed] [Google Scholar]
- 23.Zhou Q, Cui Y, Wang C, Wu H, Xiong H, Qi K, et al. Characterization of natural co-infection with goose astrovirus genotypes I and II in gout affected goslings. Avian Pathol. (2024) 53:146–53. doi: 10.1080/03079457.2023.2295341, [DOI] [PubMed] [Google Scholar]
- 24.Khameneh HJ, Ho AW, Laudisi F, Derks H, Kandasamy M, Sivasankar B, et al. C5a regulates IL-1β production and leukocyte recruitment in a murine model of monosodium urate crystal-induced peritonitis. Front Pharmacol. (2017) 8:10. doi: 10.3389/fphar.2017.00010, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Zhang J, Kaiser MG, Gallardo RA, Kelly TR, Dekkers JCM, Zhou H, et al. Transcriptome analysis reveals inhibitory effects of Lentogenic Newcastle disease virus on cell survival and immune function in spleen of commercial layer chicks. Genes (Basel). (2020) 11:1003. doi: 10.3390/genes11091003, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Onomoto K, Onoguchi K, Yoneyama M. Regulation of RIG-I-like receptor-mediated signaling: interaction between host and viral factors. Cell Mol Immunol. (2021) 18:539–55. doi: 10.1038/s41423-020-00602-7, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Zheng J, Shi W, Yang Z, Chen J, Qi A, Yang Y, et al. RIG-I-like receptors: molecular mechanism of activation and signaling. Adv Immunol. (2023) 158:1–74. doi: 10.1016/bs.ai.2023.03.001, [DOI] [PubMed] [Google Scholar]
- 28.Wang Z, Li L, Liu P, Wang C, Lu Q, Liu L, et al. Host innate immune responses of geese infected with goose origin nephrotic astrovirus. Microb Pathog. (2021) 152:104753. doi: 10.1016/j.micpath.2021.104753, [DOI] [PubMed] [Google Scholar]
- 29.Li Y, Wu S, Tian X, Kong C, Hong W, Xiao T, et al. The structural basis of TRIM25-mediated regulation of RIG-I. J Biol Chem. (2025) 301:108367. doi: 10.1016/j.jbc.2025.108367, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Yang Z, Wang J, He B, Zhang X, Li X, Kuang E. RTN3 inhibits RIG-I-mediated antiviral responses by impairing TRIM25-mediated K63-linked polyubiquitination. eLife. (2021) 10:e68958. doi: 10.7554/eLife.68958, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Tong Z, Zou JP, Wang SY, Luo WW, Wang YY. Activation of the cGAS-STING-IRF3 Axis by type I and II interferons contributes to host defense. Adv Sci (Weinh). (2024) 11:e2308890. doi: 10.1002/advs.202308890, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Ren D, Li T, Zhang X, Yao X, Gao W, Xie Q, et al. OASL triggered by novel goose Astrovirus via ORF2 restricts its replication. J Virol. (2020) 94:e01767–20. doi: 10.1128/jvi.01767-20, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.He X, Zhang S, Zou Z, Gao P, Yang L, Xiang B. Antiviral effects of avian interferon-stimulated genes. Animals (Basel). (2024) 14:3062. doi: 10.3390/ani14213062, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Schoggins JW. Interferon-stimulated genes: what do they all do? Annu Rev Virol. (2019) 6:567–84. doi: 10.1146/annurev-virology-092818-015756, [DOI] [PubMed] [Google Scholar]
- 35.Thompson S, Martínez-Burgo B, Sepuru KM, Rajarathnam K, Kirby JA, Sheerin NS, et al. Regulation of chemokine function: the roles of GAG-binding and post-translational nitration. Int J Mol Sci. (2017) 18:1692. doi: 10.3390/ijms18081692, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Hou Z, Jin S, Liang Y, Wang H, Jiang D, Cao N, et al. Apoptosis, inflammatory and innate immune responses induced by infection with a novel goose astrovirus in goose embryonic kidney cells. Front Cell Infect Microbiol. (2024) 14:1452158. doi: 10.3389/fcimb.2024.1452158, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Urcuqui-Inchima S, Cabrera J, Haenni AL. Interplay between dengue virus and toll-like receptors, RIG-I/MDA5 and microRNAs: implications for pathogenesis. Antivir Res. (2017) 147:47–57. doi: 10.1016/j.antiviral.2017.09.017, [DOI] [PubMed] [Google Scholar]
- 38.Kato H, Takeuchi O, Sato S, Yoneyama M, Yamamoto M, Matsui K, et al. Differential roles of MDA5 and RIG-I helicases in the recognition of RNA viruses. Nature. (2006) 441:101–5. doi: 10.1038/nature04734, [DOI] [PubMed] [Google Scholar]
- 39.Loo YM, Gale M, Jr. Immune signaling by RIG-I-like receptors. Immunity. (2011) 34:680–92. doi: 10.1016/j.immuni.2011.05.003, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Meylan E, Curran J, Hofmann K, Moradpour D, Binder M, Bartenschlager R, et al. Cardif is an adaptor protein in the RIG-I antiviral pathway and is targeted by hepatitis C virus. Nature. (2005) 437:1167–72. doi: 10.1038/nature04193, [DOI] [PubMed] [Google Scholar]
- 41.Seth RB, Sun L, Ea CK, Chen ZJ. Identification and characterization of MAVS, a mitochondrial antiviral signaling protein that activates NF-kappaB and IRF 3. Cell. (2005) 122:669–82. doi: 10.1016/j.cell.2005.08.012, [DOI] [PubMed] [Google Scholar]
- 42.Ivashkiv LB, Donlin LT. Regulation of type I interferon responses. Nat Rev Immunol. (2014) 14:36–49. doi: 10.1038/nri3581, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Platanias LC. Mechanisms of type-I- and type-II-interferon-mediated signalling. Nat Rev Immunol. (2005) 5:375–86. doi: 10.1038/nri1604, [DOI] [PubMed] [Google Scholar]
- 44.Schneider WM, Chevillotte MD, Rice CM. Interferon-stimulated genes: a complex web of host defenses. Annu Rev Immunol. (2014) 32:513–45. doi: 10.1146/annurev-immunol-032713-120231, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Ding R, Xu H, Huang H, Cao R, Lv Y. Effects of goose Astrovirus type 2 infection on peripheral blood lymphocyte and macrophage activity in vitro. Viral Immunol. (2024) 37:139–48. doi: 10.1089/vim.2023.0098, [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Summary of RNA-seq sequencing quality metrics for each spleen sample.
PLS-DA explained variance, cross-validation, and permutation-test results.
Expression profiles and annotations of all expressed genes.
Differentially expressed genes between GAstV-infected and control gosling spleens.
GO enrichment analysis of differentially expressed genes.
KEGG pathway enrichment analysis of differentially expressed genes.
GSEA results based on ranked expressed genes.
Data Availability Statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: PRJNA1479086 (NCBI-BioProject-SRA).