Transcriptome Analysis Reveals New Insights of Novel Duck Reovirus (NDRV)-Infected Duck Primary Macrophages

Transcriptome Analysis Reveals New Insights of Novel Duck Reovirus (NDRV)-Infected Duck Primary Macrophages | 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 Transcriptome Analysis Reveals New Insights of Novel Duck Reovirus (NDRV)-Infected Duck Primary Macrophages Kaikai Han, Fengying Lu, Lijiao Zhang, Dongmin Zhao, Xinmei Huang, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9362542/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Novel duck reovirus (NDRV) is a newly identified reovirus that can cause hemorrhage and necrosis on the spleen of duck or goose, this disease has resulted in serious economic losses to the duck industry in China. Up to now, there is no effective vaccine against NDRV infection. To better understand the host cellular responses and the pathogenesis of NDRV infection, we performed transcriptomic profiling to compare gene expression changes between NDRV-infected and mock-infected duck primary macrophages. Duck primary macrophages were isolated and divided into three groups: mock-infected control, 12 hours post-infection (hpi), and 24 hpi. Differentially expressed genes (DEGs) in response to NDRV infection were identified using RNA sequencing (RNA-Seq). A total of 3047 DEGs were identified, including 1834 up-regulated and 1213 down-regulated genes at 12 hpi. In addition, a total of 2514 DEGs were identified at 24 hpi, including 1554 up-regulated and 960 down-regulated genes. Gene Ontology (GO) analysis showed that DEGs can be divided into the molecular function, cellular component and biological process. Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed that these DEGs were primarily involved in immune response, cellular metabolic processes, and signal transduction pathways, including TNF signaling, Toll-like receptor signaling, and JAK-STAT signaling pathways. Importantly, the expressions of inflammation-related genes and various interferon-stimulated genes (ISGs) were up-regulated after NDRV infection. Moreover, some selected DEGs were further examined by real-time PCR and the results were consistent with the RNA-Seq data. This study provides the first comprehensive transcriptomic profile of duck primary macrophages in response to NDRV infection, offering new insights into the molecular mechanisms of host-pathogen interactions and potential therapeutic targets for NDRV infection. novel duck reovirus duck primary macrophages Transcriptome analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Duck reovirus (DRV), a member of the genus Orthoreovirus in the family Reoviridae , is a common waterfowl virus with various morbidities [ 1 , 2 ]. DRV was first identified in France (named Muscovy Duck Reovirus, MDRV), now has been found in many parts of the world, mainly in Europe and Asia [ 2 , 3 ]. DRV can be phylogenetically classified into two distinct genotypes based on biological and genetic characteristics: genotype I (Classical DRV, CDRV) and II (Novel DRV, NDRV) [ 4 ]. To date, CDRV has been exclusively identified in Muscovy ducks and domestic geese, resulted in lameness, growth retardation, and immunosuppression, leading to increased susceptibility to secondary infections [ 5 , 6 ]. In recent years, a novel duck reovirus (NDRV), isolated from infected ducklings (including Pekin ducks, Cherry Valley ducks and geese), causes high mortality in ducklings but remains asymptomatic in adult ducks, has been frequently reported in southern China [ 7 – 9 ]. First identified in 2005 in Fujian, Guangdong, and Zhejiang provinces, NDRV has since spread rapidly across China’s major duck farms, becoming endemic in these regions [ 10 ]. The morbidity and mortality rates range from 5%-35% and 2%-20%, respectively [ 11 ]. NDRV infection induces spleen necrosis and bursal pathology, leading to immunosuppression and increased susceptibility to secondary bacterial infections [ 12 , 13 ]. Currently, there is still no effective vaccine available against NDRV infection. The DRV genome is composed of three large double-stranded RNA segments, three medium- sized segments, and four small segments. It encodes at least 10 structural proteins and 4 non- structural proteins with distinct electrophoretic mobilities [ 14 ]. Nevertheless, the regulatory mechanisms underlying ducklings' responses to NDRV infection remain largely elusive. The intricate interplay between viruses and hosts is pivotal for elucidating the mechanisms of viral pathogenesis. Transcriptomic and proteomic analyses have emerged as powerful and efficient tools for large-scale profiling of host gene and protein expression dynamics following viral infection. For example, two-dimensional polyacrylamide gel electrophoresis revealed 59 differentially expressed proteins in Muscovy duck embryo fibroblasts infected with MDRV, associated with carbohydrate and nucleotide metabolism, stress response, and immune regulation [ 15 ]. MDRV infection significantly up-regulated fatty acid degradation-related genes, including ATP binding cassette transport G8 and apolipoprotein A-IV, as well as chemotaxis cytokine receptor genes (CCR7, CCR9, CCR10) [ 15 ]. The iTRAQ-LC-MS/MS analysis was employed to identify the protein profiles of NDRV-infected duck embryo fibroblasts (DEFs). At 24 h post-infection, 179 proteins (89 upregulated and 90 downregulated with a 1.5 - fold change cutoff) were found, NDRV infection activates the innate immune responses of the host cell, leading to the expression of various ISGs [ 16 ]. Yun et al. identified serine protease-associated proteins responding to C/NDRV infections in both liver and spleen using an advanced MS/MS-based approach. Furthermore, C/NDRV infections markedly increased expression of glycolysis-related enzymes, fructose bisphosphate aldolase and pyruvate kinase [ 17 ]. The spleen, which is rich in peripheral blood lymphocytes and macrophages, is the largest and most important peripheral immune organ in ducks. Previous studies have found that NDRV infection can cause harm to some organs of infected ducks, the viral load in the spleen and liver after NDRV infection was noticeably above those in the lung and heart [ 7 , 17 ]. Macrophages, as key phagocytic cells, play a pivotal role in host defense by engulfing and eliminating infectious agents, clearing cellular debris, and facilitating adaptive immunity through antigen presentation to T cells. These heterogeneous immune cells are ubiquitously distributed across all tissues and contribute to diverse physiological processes, including homeostasis, innate immune responses, tissue remodeling, and wound repair [ 18 , 19 ]. Their primary function, however, remains the orchestration of antimicrobial defense mechanisms to combat pathogenic invasions. Up to now, the role of macrophages in NDRV infection of healthy ducks remains unclear. In our research, in order to acquire transcriptomic information and discover the interactions of NDRV with host cells, duck macrophages were isolated and infected with NDRV, harvested at 12, 24 h post-infection (hpi), and subjected to transcriptomic sequencing. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were utilized to examine differentially expressed genes (DEGs) identified during different stages of infection. We hope our findings are served as a resource for improving the understanding of the effects of NDRV on immunosuppression in waterfowl and are of significant benefit to vaccine design and the development of antiviral drugs. 2. Materials and methods 2.1 Cell culture and NDRV preparation Duck primary macrophages were isolated from the peripheral blood of healthy ducks using density gradient centrifugation. Briefly, blood was collected in heparinized tubes and diluted with phosphate-buffered saline (PBS). The diluted blood was layered over Ficoll-Paque PLUS (GE Healthcare) and centrifuged at 1500 rpm for 30 min at room temperature. The mononuclear cell layer was collected and washed twice with PBS. Cells were then resuspended in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS), 100 U/ml penicillin, and 100 µg/ml streptomycin. The cells were seeded in 6-well plates and incubated at 37°C in a 5% CO 2 humidified incubator for 2 h to allow macrophages to adhere. Non-adherent cells were removed by washing with PBS, and the adherent macrophages were cultured for further experiment. The NDRV originally isolated in Jiangsu Province in August 2008, was propagated in BHK-21 cells to a titer of 10 5.5 TCID 50 /0.1ml and maintained in our laboratory. 2.2 Virus Infection Duck primary macrophages (2×10 5 cells) were seeded in 6-well plates, grown to approximately 80%-90% confluency, and infected with NDRV at a multiplicity of infection (MOI) of 1. After 1 h of viral absorption at 37°C in a 5% CO 2 incubator, the inoculum was replaced with maintenance medium (DMEM plus 2% of FBS), and then harvested at 12 and 24 hpi. Uninfected cells were synchronized collected as a control. Each group was processed with three independent biological replicates. 2.3 RNA Extraction, library construction and sequencing Total RNA was isolated from duck primary macrophages of each experimental group using the Trizol reagent kit (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s instructions. RNA integrity and quality were evaluated using an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA) and further verified by RNase-free agarose gel electrophoresis. Following RNA extraction, eukaryotic mRNA was enriched using Oligo (dT) beads, whereas prokaryotic mRNA was enriched by depleting ribosomal RNA (rRNA) with the Ribo-Zero™ Magnetic Kit (Epicenter, Madison, WI, USA). The enriched mRNA was subsequently fragmented into short segments using a fragmentation buffer and reverse-transcribed into complementary DNA (cDNA) using random primers. Second-strand cDNA synthesis was performed using DNA polymerase I, RNase H, and deoxynucleotide triphosphates (dNTPs). The resulting cDNA fragments were purified using the QiaQuick PCR extraction kit (Qiagen, Venlo, The Netherlands), followed by end repair, poly (A) tailing, and ligation to Illumina sequencing adapters. The ligated products were size-selected via agarose gel electrophoresis, amplified by PCR, and subjected to high-throughput sequencing on the Illumina HiSeq2500 platform (Gene Denovo Biotechnology Co., Guangzhou, China). 2.4 Transcriptomic Data Analyses To ensure the acquisition of high-quality clean data, the RNA-Seq-derived datasets were subjected to stringent filtration utilizing fastp (version 0.18.0, accessible at https://github.com/OpenGene/fastp ). Subsequent to this filtration, the resultant clean datasets were employed for downstream processes including transcriptome assembly and the quantification of gene abundance. The unigenes assembled from these datasets were aligned against the Anas platyrhynchos (duck) reference genome (Ensembl_release110) employing HISAT2 version 2.2.4, with the parameter "-rna-strandness RF" alongside default settings. Alignment of reads for each specimen was conducted using StringTie version 1.3.1, adopting a reference-guided assembly strategy. Expression levels and variability of each transcriptional unit were quantified through the calculation of FPKM (Fragments Per Kilobase of transcript per Million mapped reads) values, facilitated by StringTie. Consequently, the differential expression analysis between NDRV-infected and mock-infected cohorts was executed and normalized utilizing the FPKM methodology, as delineated by Mortazavi et al., 2008 [ 20 ]. 2.5 Differential Expression Analysis Differential expression analysis was conducted on data obtained from distinct groups at 12 and 24 hpi utilizing the DESeq2 software. Genes exhibiting a false discovery rate (FDR) of less than 0.05 and an absolute fold change of 2 or greater were identified as differentially expressed genes (DEGs). The p -values were adjusted using the FDR method to control for multiple testing. To elucidate the biological functions of the DEGs, these genes were annotated against the Gene Ontology (GO) database (accessible at http://www.geneontology.org/ ), and the frequency of genes associated with each GO term was quantified. This process yielded a catalog of genes annotated with specific GO functions and their corresponding counts. Subsequently, a hypergeometric distribution test was applied to identify GO categories significantly overrepresented among the DEGs relative to the entire genome. Furthermore, the Kyoto Encyclopedia of Genes and Genomes (KEGG) online annotation tool was employed to pinpoint pathways significantly enriched in the DEGs compared to the whole genome. For both GO and KEGG analyses, a corrected p -value threshold of less than 0.05 was established to denote statistical significance. 2.6 Immunofluorescence assay Immunofluorescence assay was performed as described previously [ 21 ]. Briefly, the cells were fixed and permeabilized by incubation in cold methanol at -20°C for 10 min. Following fixation, the cells were washed thoroughly and subsequently blocked with 1% bovine serum albumin (BSA) to minimize nonspecific binding. The cells were then incubated with appropriately diluted primary antibodies at 37°C for 2 h. After three washes with PBS, the cells were incubated with corresponding Alexa Fluor-conjugated secondary antibodies for 1 h at room temperature in the dark. Prepared Samples were observed under an inverted fluorescence microscope. 2.7 Real-Time RT-PCR Gene expression levels were quantified using quantitative PCR (qPCR) with the SYBR Premix Ex Taq™ II Kit (Takara, China) on a Light Cycler 480 real-time PCR system (Roche). Specific primers for amplifying target genes identified by RNA-Seq were designed based on gene sequences retrieved from GenBank, utilizing Lasergene sequence analysis software (DNAStar, Inc., Madison, WI, USA; Table 1 ). The qPCR reactions were performed in a final volume of 20 µl, containing 0.8 µl of each primer and 1 µl of cDNA template. The thermal cycling protocol consisted of an initial activation step at 95°C for 30 s, followed by 40 cycles of denaturation at 95°C for 10 s, and annealing/extension at 60°C for 30 s. Melting curve analysis was conducted to verify amplification specificity, and relative gene expression levels were calculated using the 2 −ΔΔCT method. Mock-infected HD11 cells served as the control group, with their expression levels normalized to 1. All reactions were performed in triplicate to ensure reproducibility. 2.8 Statistical analyses Data are expressed as the mean ± standard error of the mean (SEM). Statistical differences between the control and treated groups were analyzed using the Student’s t-test. A p-value of less than 0.05 (p < 0.05) was considered statistically significant. 3. Results 3.1 NDRV replicated in Duck primary macrophages We first determined whether duck primary monocyte-derived macrophages are permissive to NDRV infection in vitro . Duck primary macrophages were infected with the virus for different time points at MOI = 1. Infection and mock-infection were performed in biological triplicate for each time point and total RNA was extracted from both groups. From 12 hpi, a number of immunofluorescent cells could be detected after IFA staining using anti-NDRV antiserum (Fig. 1 ). No fluorescence was observed in mock-infected cells (Fig. 1 ). The replication of the viral genome was determined by RT-qPCR and the fold change, the results showed that the virus begins to replicate rapidly from 12 hpi (Fig. 1 ). To obtain an obviously changed transcriptome profile and minimize the influence of cell death and lysis, data at 12 hpi and 24 hpi were choosed and analyzed by RNA-Seq. 3.2 RNA-Seq results Following a comprehensive quality assessment, mRNA was isolated from total RNA utilizing poly-T oligo-conjugated magnetic beads. Subsequently, a cDNA library was synthesized and subjected to rigorous quality evaluation. After cluster generation, the library preparations were sequenced on an Illumina HiSeqTM 2000 platform, yielding 150 bp paired-end reads. Each sequencing run generated a minimum of 40 million high-quality clean reads, with ≥ 97% of the reads achieving a Q-score > 20 and ≥ 93% of the reads achieving a Q-score > 30 (Table 2 ). Importantly, all samples had between 89.47% and 91.60% of total reads mapped to the duck reference genome. All of these results illustrated the high quality of the sequencing data and ensured their suitability for the next step of the analysis. The mapped data was normalized by calculating the FPKM and the distribution of mean FPKM per gene was found to be uniform. The correlation of gene expression levels between all of the samples was performed using the squared Pearson correlation coefficient (R 2 ), revealed a minimum correlation value of 0.821 (Fig. 2 ). These results demonstrate that the expression levels of different genes or groups of genes are comparable, indicating that the treatment is repeatable and favorable. 3.3 Differentially expressed genes upon NDRV infection To further investigate the differential expression patterns in duck primary macrophages between infected and mock-infected samples, the normalized gene expression level data were analyzed. Herein, the genome of Anas platyrhynchos (duck) (Ensembl_release110) was utilized as our reference genome. The distinct effect on gene expression upon ARV infection was carefully analyzed under the threshold of p < 0.05 & fold changes ≥ 2. Afterwards, the infected and mock-infected were compared with each other and the outline of the DEGs are listed in Table S1 . Compared with mock infected controls, 3047 changes in the transcriptome, with 1834 up-regulated and 1213 down-regulated DEGs, were observed in response to NDRV infection at 12 hpi. 2514 changes were observed, with 1554 up-regulated and 960 down-regulated DEGs at 24 hpi. Compared with 12 hpi, 267 changes were observed, with 110 up-regulated and 157 down-regulated DEGs at 24 hpi. Also, these results were clearly visualized by clustering the samples by differential treatment and by constructing a volcano plot of the DEGs (Fig. 3 ). 3.4 GO annotation of DEGs upon NDRV infection All the DEGs were classified into various gene ontology (GO) terms representing three major categories: ‘molecular function’, ‘cellular component’ and ‘biological process’. Compared with mock infected controls, 28 GO terms in the biological process category, 3 GO terms in the cellular component category, and 19 GO terms in the molecular functions category were determined to be significantly enriched (P < 0.05) in response to NDRV infection at 12 hpi (Fig. 4 A). The significantly enriched ‘biological process’ GO terms were associated with ‘cellular process’, ‘biological regulation’, ‘metabolic process’, ‘regulation of biological process’, ‘response to stimulus’, ‘mutlicellular organism process’, ‘signaling’ and ‘localization’; within the ‘molecular function’ category, the most represented GO terms were ‘binding’, ‘catalytic activity’, ‘molecular function regulator’ and ‘transcription regulator activity’. Also, the most enriched GO terms within the ‘cellular component’ category were ‘cellular anatomical entity’ and ‘protein-containing complex’ (Fig. 4 A). Compared with mock infected controls, 28 GO terms in the biological process category, 3 GO terms in the cellular component category, and 17 GO terms in the molecular functions category were determined to be significantly enriched (P < 0.05) in response to NDRV infection at 24 hpi (Fig. 4 B). Furthermore, the differences in gene accumulation between 12 hpi and 24 hpi upon NDRV were compared, the most represented GO terms are similar to 12 hpi-vs-mock infected controls (Fig. 4 C). 3.5 Pathway analysis of DEGs upon NDRV infection To explore the various biological processes involved in NDRV infection, the differentially expressed genes were mapped into canonical signalling pathways using KEGG analysis. Under NDRV 12 hpi, the enriched DEGs were mainly involved in either disease pathways or host immune response, including cytokine-cytokine receptor interaction, TNF signaling pathway, viral protein interaction with cytokine and cytokine receptor, JAK-STAT signaling pathway, Toll-like receptor signaling pathway, Influenza A, Lipid and atherosclerosis, Kaposi sarcoma-associated herpesvirus infection, Human cytomegalovirus infection, pathway in cancer and NOD-like receptor signaling pathway (Fig. 5 A). Under NDRV 24 hpi, the KEGG terms were relatively similar to those at 12 hpi, albeit with slight differences in their order (Fig. 5 B). Collectively, these findings indicate that both viral agents and host cellular components employ distinct molecular strategies, which may contribute to the pathogenic mechanisms associated with NDRV infection. 3.6 Validation of RNA-Seq data by real-time qRT-PCR analysis To validate the differential gene expression profiles obtained by RNA-Seq, expression of various genes involved in interferon stimulated gene (Mx1, IFIT5, OASL, CH25H), inflammatory response (TNF-α, PKR, IL1β, IL6, IL8, CCL4, CCL19, CCL20) and innate immunity (IFN-α, IFN-β, IFN-γ, MDA5, TLR7) were examined by qRT-PCR in HD11 cells. The data demonstrate that the overall results of qRT-PCR were consistent with those of the RNA-Seq (Fig. 6 ). Although several fold differences was observed between these two types of analysis because of intrinsic differences between the techniques and cell line. From the above results, it can be seen that the infiltrated macrophages, inflammatory cytokines including TNF-α, IL1β, IL6, and IL8, and emerging interactions of various cell populations through CCL4, CCL19, and CCL20, may contribute to the NDRV driven inflammatory spleen injury. 4. Discussion The emerging infectious disease caused by NDRV was first reported in several southern provinces of China in 2005, posing an enormous threat to the domestic poultry industry [ 1 , 10 ]. The disease caused by NDRV is an acute and contact infectious disease in duck farms, clinically characterized by splenic enlargement, necrotic lesions, and elevated mortality rates [ 7 , 10 ]. Information about the pathogenesis of NDRV and host-virus interactions has been poorly understood, though Yun identified a number of proteins responsive to the CDRV/NDRV infections by high throughput proteomics analysis using the control and infected spleen cells [ 17 ]. In the current study, we tried to build a complete expression profile of NDRV-mediated changes at the transcriptional level using RNA-Seq method to unveil the complex interactions between NDRV and host cells. In the previous study, RNA-Seq technology was applied to investigate the transcriptome-wide changes of DF-1 cells upon ARV infection at 10 hpi and 18 hpi [ 22 ]. At 10 hpi, 104 genes were down-regulated and 64 were up-regulated, while 47 genes were up-regulated and only one was down-regulated at 18 hpi in the ARV-infected cells. These DEGs had functions in innate immunity, anti-stress and energy metabolism. In the present study, we used transcriptomic analysis to obtain global information on duck primary macrophages infected with NDRV at 12 hpi and 24 hpi. A total of 3047 DEGs were identified, with 1834 up-regulated and 1213 down-regulated DEGs were observed in response to NDRV infection at 12 hpi. 2514 changes were identified, with 1554 up-regulated and 960 down-regulated DEGs at 24 hpi. This indicates that, with the progression of technological advancements, an increasing number of DEGs can be identified, thereby elucidating the dynamic changes in host cells upon viral infection. The DEGs induced by NDRV infection were mainly involved in molecular function, cellular component, and biological process through the GO analysis. The signaling pathways of host innate immune system response and inflammatory response were observed significantly up-regulated according to the KEGG pathway analysis. The expression levels of selected DEGs within these pathways were validated using qPCR, and the results were consistent with the KEGG pathway analysis. Macrophages are highly versatile immune cells that play pivotal roles in host defense, tissue homeostasis, and immune regulation. Derived from circulating monocytes, macrophages undergo context-dependent differentiation into distinct functional subsets, primarily classified as classically activated (M1) or alternatively activated (M2) macrophages [ 23 ]. This plasticity allows macrophages to adapt dynamically to microenvironmental cues, but it also renders them susceptible to exploitation by viral pathogens. Viruses have evolved sophisticated strategies to hijack macrophage machinery for their replication. For instance, human immunodeficiency virus (HIV) exploits CD4 and CCR5/CXCR4 co-receptors to infect macrophages, establishing persistent reservoirs that evade immune clearance [ 24 ]. Similarly, influenza A virus (IAV) engages sialic acid receptors to enter macrophages, where inefficient replication can trigger excessive cytokine production, contributing to immunopathology [ 25 ]. Macrophage tropism is also observed in dengue virus (DENV) infection, wherein antibody-dependent enhancement (ADE) facilitates viral entry, leading to enhanced replication and systemic inflammation [ 26 ]. Importantly, many viruses manipulate macrophage polarization to favor their survival; for example, hepatitis C virus (HCV) promotes an M2-like phenotype to dampen antiviral responses [ 27 ]. Macrophages also play a key role in avian viral infections including avian influenza virus (AIV) [ 28 ], Newcastle disease virus (NDV) [ 29 ], infectious bursal disease virus (IBDV) [ 30 ], infectious bronchitis virus (IBV) [ 31 ] and avian leukosis virus subgroup J (ALV-J) [ 32 ]. However, the role of macrophages in NDRV infection remains unclear. In the present study, the transcriptomic profiling elucidated an immune response diagram upon NDRV infection. Genes that were differentially expressed during NDRV infection can potentially provide insights into the complex regulatory phenomena. Initially, we found that NDRV infection activated many pattern recognition receptors (PRR) pathways including RIG-I-like receptors (RLR), NOD-like receptors (NLR) and Toll-like receptors (TLR). TLR7, an intracellular sensor of exogenous single-stranded RNA, is known to be modulated by various viruses including Tembusu virus (TMUV) [ 33 ], ALV-J [ 32 ] and H9N2 avian influenza virus [ 34 ]. Upon activation, TLR signaling pathways induce robust IFN production, playing a pivotal role in antiviral defense. In contrast to TLRs that are predominantly expressed in immune cells, RLRs are ubiquitously distributed in the cytoplasm of both immune and non-immune cells, functioning as independent viral sensors. RLRs mediate antiviral responses through viral RNA detection and subsequent activation of type I interferons and inflammatory cytokines. Furthermore, we tested MDA5, which also detects exogenous dsRNA, via RT-qPCR and found that it responded to NDRV, suggesting NDRV-derived dsRNA generated during viral replication is detected by these cytosolic sensors. NDRV were recognized by PRRs and captured by antigen-presenting cells (APCs), triggering the downstream signaling cascades and production of cytokines. IFN-α and IFN-γ, two types of antiviral cytokines, were up-regulated after viral infection, activating the JAK-STAT signaling pathways and inducing the transcription of several ISGs, such as Mx, PKR, CH25H, IFIT5 and OASL at 12 hpi and 24 hpi. ISGs exert broad antiviral effects by targeting diverse stages of the viral life cycle. For instance, Mx can prevent viral replication by trapping viral essential components and blocking the nuclear import of nucleocapsids or interacting with viral ribonucleoprotein structures [ 34 ]. CH25H inhibits enveloped viruses (e.g., VSV, HSV, HIV, EBOV, RVFV, RSSEV) via cholesterol conversion to 25-hydroxycholesterol (25HC) [ 35 ], while PKR, an IFN-induced dsRNA-dependent kinase, suppresses viral replication by phosphorylating EIF2α to block translation initiation [ 21 ]. These findings underscore the critical role of the interferon signaling pathway in restricting NDRV replication, consistent with its antiviral function against other viruses [ 36 ]. As we know, virus infections induce a proinflammatory response including expression of chemokines and cytokines [ 37 ]. In this study, increased expression of tumor necrosis factor-α (TNF-α), IL-1β, IL-6, IL-8, CCL20, CCL19, caspase 3, SOCS1 and SOCS3 were observed after NDRV infection. TNF-α is a pleiotropic proinflammatory cytokine primarily produced by activated macrophages, T lymphocytes, and NK cells during immune responses [ 38 ]. As a pivotal mediator of innate immunity, TNF-α exerts its biological effects through binding to two distinct receptors: TNFR1 (p55) and TNFR2 (p75). In concert with chemokines, TNF and TNFRs are involved in the regulation of inflammatory processes like arthritis, of infectious diseases such as HIV infection, and of malignancies [ 39 ]. CCL19 and CCL20 are chemokines belonging to the CC chemokine family which were detected in our transcriptome profiles. CCL19 mediates multiple immunological processes, including physiological lymphocyte recirculation, thymic T cell homing, and the migration of both T and B lymphocytes to secondary lymphoid organs [ 40 ]. Previous studies have indicated that NDRV induces excessive inflammatory responses and causes tissue damage in the spleen and liver [ 17 ]. In our study, a pro-inflammatory mediator (IL-6) and an anti-inflammatory mediator (IL-10) were significantly up-regulated upon NDRV infection. IL-6 is a pleiotropic cytokine with pivotal roles in immune regulation, cell growth, and differentiation [ 41 ]. Dysregulation of IL-6 and its receptor signaling contributes to the pathogenesis of various diseases, including immunorepressive disorders and malignancies [ 42 ]. Elevated IL-6 expression has been documented during infections with certain reoviruses and avian immunosuppressive viruses [ 43 ]. Inflammation in the spleen is known to be triggered by apoptotic processes, given the high abundance of peripheral blood lymphocytes and macrophages in this organ [ 44 ]. We therefore hypothesized that NDRV infection induces splenocyte apoptosis, which in turn drives splenic inflammation, ultimately leading to tissue damage due to excessive inflammatory responses. As part of the innate immune defense against viral invasion, a robust production of cytokines and chemokines is elicited. Consistent with this, GO enrichment analysis revealed significant upregulation of cytokine- and chemokine-encoding genes during NDRV infection, suggesting their involvement in antiviral immunity. Interestingly, we observed that expression of SOCS1 and SOCS3 was significantly up-regulated upon NDRV infection. SOCS-1 and SOCS-3, members of the STAT-induced STAT inhibitor (SSI) family (also termed suppressors of cytokine signaling), function as negative regulators of the JAK-STAT signaling pathway [ 45 ]. Our findings demonstrate their activation during infection, suggesting these molecules may protect the host from immunopathological damage through negative feedback regulation of excessive immune responses. In summary, our study provided valuable information regarding the transcriptome of macrophages against NDRV infection. Our results from transcriptomic analysis showed that DEGs related to innate immune response, including RLR, NLR signaling pathway, NF-κB signaling pathway, Jak-STAT signaling pathway and inflammation related pathway were participated in NDRV infection. Moreover, the expression profiles of inflammation related gene during NDRV infection were validated by qRT-PCR. Taken together, our data provided new insights into understanding the potential responses of immune-related genes against NDRV infection. Declarations Acknowledgments This work was supported by the open project of the key laboratory of prevention and control of major poultry diseases (Avian Influenza), Ministry of Agriculture and Rural Affairs (Grant No. YDWS202214). Author contributions Han K and Lu F conceived the study and edited the manuscript. Han K, Zhang L and Zhao D wrote the manuscript. Huang X, Wu F and Yang J prepared the figures and tables. Yin X, Su D and Liu Y helped to carry out the cell-culture and data analysis. Zhang X and Liu Q revised the manuscript. All authors approved the final version of the manuscript. Conflict of interest The authors declare no conflict of interest. References Cao Y, Sun M, Wang J, Hu X, He W, Su J (2019) Phenotypic and genetic characterisation of an emerging reovirus from Pekin ducks in China. Sci Rep 9 (1): 7784. Varga-Kugler R, Marton S, Thuma A, Szentpali-Gavaller K, Balint A, Banyai K (2022) Candidate ‘avian orthoreovirus b’: An emerging waterfowl pathogen in europe and asia? ‌TransboundEmerg Dis 69 (5): e3386-e3392. Gaudry D, Charles JM, Tektoff J (1972) A new disease expressing itself by a viral pericarditis in barbary ducks. C R Acad Hebd Seances Acad Sci D 274 (21): 2916–2919. Yun T, Yu B, Ni Z, Ye W, Chen L, Hua J et al (2014) Genomic characteristics of a novel reovirus from muscovy duckling in china. Vet Microbiol 168 (2–4): 261–271. Chen X, Zheng M, Lin F, Cheng X, Xiao S, Chen S et al (2019) Impacts of novel duck reovirus infection on the composition of intestinal microbiota of Muscovy ducklings. Microb Pathog 137: 103764. Wu Y, Liu Z, Zhu E, Li M, Jiang H, Luo Y et al (2019) Changes in the small intestine mucosal immune barrier in muscovy ducklings infected with muscovy duck reovirus. Vet Microbiol 233: 85–92. Wang H, Gao B, Liu X, Zhang S, Diao Y, Tang Y (2020) Pathogenicity of a variant duck orthoreovirus strain in Cherry Valley ducklings. Vet Microbiol 242: 108546. Li N, Hong T, Wang Y, Wang Y, Yu K, Cai Y et al (2016) The pathogenicity of novel duck reovirus in Cherry Valley ducks. Vet Microbiol 192: 181–185. Yun T, Hua J, Ye W, Yu B, Chen L, Ni Z et al (2018) Comparative proteomic analysis revealed complex responses to classical/novel duck reovirus infections in Cairna moschata. Sci Rep 8 (1): 10079. Kong J, Shao G, Zhang Y, Wang J, Xie Z, Feng K et al (2023) Molecular characterization, complete genome sequencing, and pathogenicity of novel duck reovirus from south coastal area in China. Poult Sci 102 (8): 102776. Wang W, Liang J, Shi M, Chen G, Huang Y, Zhang Y et al (2020) The diagnosis and successful replication of a clinical case of duck spleen necrosis disease: An experimental co-infection of an emerging unique reovirus and salmonella indiana reveals the roles of each of the pathogens. Vet Microbiol 246: 108723. Luo D, Liu R, Weng L, Li K, Qi X, Gao Y et al (2021) Genomic sequences and pathogenic characteristics of two variant duck reoviruses associated with spleen necrosis. Infect Genet Evol 92: 104847. Huang Y, Zhang J, Dong J, Li L, Kuang R, Sun M et al (2022) Isolation and characterization of a new goose orthoreovirus causing liver and spleen focal necrosis in geese, China. Transbound Emerg Dis 69 (5): 3028–3034. Benavente J, Martínez-Costas J (2007) Avian reovirus: structure and biology. Virus Res 123:105–119 Huang M, Cheng X, Chen S, Zheng M, Chen S (2017) Analysis of differentially expressed proteins in Muscovy duck embryo fibroblasts infected with virulent and attenuated Muscovy duck reovirus by two-dimensional polyacrylamide gel electrophoresis. J Vet Med Sci 79 (12): 2063–2069. Yang Y, Li L, Liu X, Jiang M, Zhao J, Li X et al (2020) Quantitative proteomic analysis of duck embryo fibroblasts infected with novel duck reovirus. Front Vet Sci 7: 577370. Yun T, Hua J, Ye W, Yu B, Ni Z, Chen L (2019) Comparative proteomic analysis revealed complex responses to classical/novel duck reovirus infections in the spleen tissue of Cairna moschata. J Proteomics 193: 162–172. Wynn TA, Vannella KM (2016) Macrophages in Tissue Repair, Regeneration, and Fibrosis. Immunity 44 (3): 450–462. Shapouri-Moghaddam A, Mohammadian S, Vazini H, Taghadosi M, Esmaeili SA, Mardani F et al (2018) Macrophage plasticity, polarization, and function in health and disease. J Cell Physiol 233 (9): 6425–6440. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 50: 621–628. Gao P, Liu Y, Wang H, Chai Y, Weng W, Zhang Y et al (2022) Viral evasion of PKR restriction by reprogramming cellular stress granules. Proc Natl Acad Sci USA 119 (29): e2201169119. Niu X, Wang Y, Li M, Zhang X, Wu Y (2017) Transcriptome analysis of avian reovirus-mediated changes in gene expression of normal chicken fibroblast DF-1 cells. BMC Genomics 18 (1): 911 Heykers A, Leemans A, Gucht W, Schryver M, Cos P, Delputte P (2019) Differences in Susceptibility of Human and Mouse Macrophage Cell Lines to Respiratory Syncytial Virus Infection. Intervirology 62 (3–4): 134–144. Decalf J, Desdouits M, Rodrigues V, Gobert F-X, Gentili M, Marques-Ladeira S et al (2017) Sensing of HIV-1 entry triggers a type I interferon response in human primary macrophages. J Virol 91 (15): e00147-17. Ruscitti C, Abinet J, Maréchal P, Meunier M, de meeûs C, Vanneste D (2024) Recruited atypical Ly6G+ macrophages license alveolar regeneration after lung injury. Sci Immunol 9(98):eado1227 Khanam A, Gutiérrez-Barbosa H, Lyke K, Chua J (2022) Immune-mediated pathogenesis in dengue virus infection. Viruses 14 (11): 2575. Bility MT, Nio K, Li F, McGivern DR, Lemon SM, Feeney ER et al (2016) Chronic hepatitis C infection-induced liver fibrogenesis is associated with M2 macrophage activation. Sci Rep (6): 39520. Kawasaki T, Ikegawa M, Yunoki K, Otani H, Ori D, Ishii KJ et al (2022) Alveolar macrophages instruct CD8+ T cell expansion by antigen cross-presentation in lung. Cell Rep 41 (11): 111828. Zhang P, Ding Z, Liu X, Chen Y, Li J, Tao Z et al (2018) Enhanced replication of virulent Newcastle disease virus in chicken macrophages is due to polarized activation of cells by inhibition of TLR7. Front Immunol 9: 366. Liu A, Li H, Qi X, Wang Q, Yang B, Wu T et al (2019) Macrophage migration inhibitory factor triggers inflammatory responses during very virulent infectious bursal disease virus infection. 10:2225 Front Microbiol Han X, Tian Y, Guan R, Gao W, Yang X, Zhou L et al (2017) Infectious bronchitis virus infection induces apoptosis during replication in chicken macrophage HD11 cells. Viruses 9 (8): 198. Yang K, Yang J, Zhou D, Zhu M, Du X, Zhou J et al (2022) Interaction of p10/p27 with macrophage migration inhibitory factor promotes avian leukosis virus subgroup J infection. Vet Microbiol 267: 109389. He Y, Wang A, Chen S, Wu Z, Zhang J, Wang M et al (2017) Differential immune-related gene expression in the spleens of duck Tembusu virus-infected goslings. Vet Microbiol 212: 39–47. Chen S, Wang A, Sun L, Liu F, Wang M, Jia R et al (2016) Immune-related gene expression patterns in GPV- or H9N2-infected goose spleens. Int J of Mol Sci 17 (12): 1990. Schneider WM, Chevillotte MD, Rice CM (2014) Interferon-stimulated genes: a complex web of host defenses. Annu Rev Immunol 32: 513–545. Wang Y, Zuo W, Zhang Y, Bo Z, Zhang C, Zhang X et al (2023) Cholesterol 25-hydroxylase suppresses avian reovirus replication by its enzymatic product 25-hydroxycholesterol. Front Microbiol 14: 1178005. Mogensen TH, Paludan SR (2001) Molecular pathways in virus-induced cytokine production. Microbiol Mol Boil Rev 65 (1): 131–150. Sedger L, McDermott M (2014) TNF and TNF-receptors: From mediators of cell death and inflammation to therapeutic giants-past, present and future. Cytokine Growth Factor Rev 25 (4): 453–472. Pasquereau S, Kumar A, Herbein G (2017) Targeting TNF and TNF receptor pathway in HIV-1 infection: from immune activation to viral reservoirs. Viruses 9 (4): 64. Rangel-Moreno J, Moyron-Quiroz J, Kusser K, Hartson L, Nakano H, Randall TD (2005) Role of CXC chemokine ligand 13, CC chemokine ligand (CCL) 19, and CCL21 in the organization and function of nasal-associated lymphoid tissue. J Immunol 175: 4904–4913. Goodman WA, Levine AD, Massari JV, Sugiyama H, McCormick TS, Cooper KD (2009) IL-6 signaling in psoriasis prevents immune suppression by regulatory T cells. J Immunol 183 (5): 3170–3176. Wu C, Chen M, Chen W, Hsieh C (2013) The role of IL-6 in the radiation response of prostate cancer. Radiat Oncol 8: 159. Miao J, Bao Y, Ye J, Shao H, Qian K, Qin A (2015) Transcriptional profiling of host gene expression in chicken embryo fibroblasts infected with reticuloendotheliosis virus strain HA1101. PLoS ONE 10 (5): e0126992. Wang H, Jiang C, Xu B, Lei D, Fang R, Tang Y (2025) Transcriptomic analysis revealed ferroptosis in ducklings with splenic necrosis induced by NDRV infection. Vet Res 56 (1): 54. Yasukawa H, Sasaki A, Yoshimura A (2000) Negative regulation of cytokine signaling pathways. Annu Rev Immunol 18: 143–164. Tables Tables are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files CvsT12.S1.xlsx CvsT24.S1.xlsx T12vsT24S1.xlsx Tables.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 04 May, 2026 Reviewers agreed at journal 02 May, 2026 Reviewers invited by journal 19 Apr, 2026 Editor assigned by journal 09 Apr, 2026 Submission checks completed at journal 09 Apr, 2026 First submitted to journal 08 Apr, 2026 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. 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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-9362542","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628544887,"identity":"7bbdbd95-d743-4118-acd2-96b326f5cc17","order_by":0,"name":"Kaikai Han","email":"","orcid":"","institution":"Jiangsu Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Kaikai","middleName":"","lastName":"Han","suffix":""},{"id":628544888,"identity":"ca97c5b4-e0da-4767-a78a-0990a2af5b44","order_by":1,"name":"Fengying Lu","email":"","orcid":"","institution":"Jiangsu Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Fengying","middleName":"","lastName":"Lu","suffix":""},{"id":628544889,"identity":"fd971c0f-3216-416a-9b26-549eddb0bdc1","order_by":2,"name":"Lijiao Zhang","email":"","orcid":"","institution":"Jiangsu Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lijiao","middleName":"","lastName":"Zhang","suffix":""},{"id":628544890,"identity":"f67830dd-81b8-4a5e-9d5b-342cc26bc046","order_by":3,"name":"Dongmin Zhao","email":"","orcid":"","institution":"Jiangsu Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Dongmin","middleName":"","lastName":"Zhao","suffix":""},{"id":628544891,"identity":"22defe34-8597-4300-9576-cb0b4143ccf0","order_by":4,"name":"Xinmei Huang","email":"","orcid":"","institution":"Jiangsu Academy of Agricultural 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Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xiaofei","middleName":"","lastName":"Zhang","suffix":""},{"id":628544901,"identity":"3759eb19-e639-4efa-8837-9225e1761130","order_by":11,"name":"Qingtao Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYLCCBAYGGTYG5gMHEn7Y8PDzN+BXzQPVwsPGwJb44GFPmozkjANEaIHQPMaGD9gO2xg0JODXYs9++OCNhztqefgkEswkEnjO8xgwHGD88DEHjy08ackWiWeO87BJJKRJJFjc5jFnbmCWnLkNn8NyzCQS246BtBwD2nKbx7LhABszLz4t/G9gWhLbJBLYzvEYHEggoEUCbEsNUEsys0EC2wEitNx4BvRL2wEeNp5njA8Se5J5JGccbMbrF/b+5IM3f7bVycm35384+OOHnT0/f/PBDx/xaAEBCQaGw8h8xgb86iFa6ggqGgWjYBSMghEMAHJXTnvReyBiAAAAAElFTkSuQmCC","orcid":"","institution":"Jiangsu Academy of Agricultural Sciences","correspondingAuthor":true,"prefix":"","firstName":"Qingtao","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-04-09 03:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9362542/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9362542/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107946544,"identity":"60558e06-13e8-42e2-b8f7-471d47532ede","added_by":"auto","created_at":"2026-04-27 23:01:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":702000,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of RNA-Seq approach. (A) Experimental setup for RNA-Seq datasets. (B) Duck primary monocyte-derived macrophages were infected with NDRV and the cytopathic effect was assessed at different time points. (C) The replication of NDRV was monitored by RT-qPCR analysis of NDRV S1 genes.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9362542/v1/c66bad005dd3913c6223087e.png"},{"id":107946546,"identity":"511714ea-2588-4c20-9f5a-a4184756d4b3","added_by":"auto","created_at":"2026-04-27 23:01:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1001952,"visible":true,"origin":"","legend":"\u003cp\u003eThe quality assessment of RNA-Seq. (A) Violin diagram of the distribution of average FPKM values per sample group. (B) The correlation between all the samples shown as a squared Pearson correlation coefficient (R\u003csup\u003e2\u003c/sup\u003e). T12: 12 hours post-infection; T24: 24 hours post-infection.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9362542/v1/1b4e32c61c9483e6a711921e.png"},{"id":108006201,"identity":"dbdb0431-2e3f-4305-a58d-2a495ad1f8cd","added_by":"auto","created_at":"2026-04-28 12:54:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":955086,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptome data profile generated by Illumina sequencing and differential expression analysis. (A) Hierarchical clustering and heatmap of genes that are differentially expressed between NDRV-infected group and mock-infected group. (B) Histogram showing screened DEGs. (C) Volcano plot displaying genes detected by RNA-Seq. The red dots and blue dots represent the unigenes that were significantly different (p\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9362542/v1/5a66416034058cacffdf1ed7.png"},{"id":107946545,"identity":"b570dded-7453-4817-b676-0eb2f2b5fc19","added_by":"auto","created_at":"2026-04-27 23:01:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1668678,"visible":true,"origin":"","legend":"\u003cp\u003eGene Ontology (GO) functional annotation and classification of differentially expressed genes (DEGs). Functional classification of DEGs was performed using GO analysis and the categories “molecular function”, “cellular component” and “biological process” were analyzed.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9362542/v1/fa985b9c4c8f96a2951b9933.png"},{"id":108006782,"identity":"9ba58d90-05ea-442e-99d6-f227caa8b65f","added_by":"auto","created_at":"2026-04-28 12:57:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1299335,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG enrichment analysis of differentially expressed genes (DEGs). The sizes indicate the number of genes assigned to the term.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9362542/v1/1f824d67d7def527e1317bb9.png"},{"id":107946547,"identity":"809dd9fc-7f0f-40a3-8381-370428dd3a0f","added_by":"auto","created_at":"2026-04-27 23:01:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1179857,"visible":true,"origin":"","legend":"\u003cp\u003eConfirmation of the transcriptome sequencing data by quantitative real‐time PCR. Seventeen differentially expressed genes (DEGs) involved in inflammatory response and innate immunity were selected for qRT‐PCR, the y-axis represents the relative expression level. The relative expression of all genes was calculated using the 2\u003csup\u003e-ΔΔCT\u003c/sup\u003e method and normalized to chicken GAPDH. Data are represented as the means ± SD. * indicates p≤ 0.05, **indicates p ≤ 0.01.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-9362542/v1/aac156ea55f0a5a4cda47485.png"},{"id":108490975,"identity":"a03be030-89f7-4e30-9b42-d73371571793","added_by":"auto","created_at":"2026-05-05 09:50:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6861196,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9362542/v1/85c8319b-df5b-4d37-a50d-290c79858e53.pdf"},{"id":107946542,"identity":"1372926a-830c-4a19-acb4-44641c79fd40","added_by":"auto","created_at":"2026-04-27 23:01:22","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":367717,"visible":true,"origin":"","legend":"","description":"","filename":"CvsT12.S1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9362542/v1/83da62cde79a4b2ae5d4e63c.xlsx"},{"id":107946543,"identity":"1123e929-b950-42bb-a1ff-ea4fe5520168","added_by":"auto","created_at":"2026-04-27 23:01:22","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":362556,"visible":true,"origin":"","legend":"","description":"","filename":"CvsT24.S1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9362542/v1/b7bff4ced3d354db240e070c.xlsx"},{"id":108007011,"identity":"698b6d88-f5da-4cce-b7ec-9df891ce7342","added_by":"auto","created_at":"2026-04-28 12:58:13","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":54488,"visible":true,"origin":"","legend":"","description":"","filename":"T12vsT24S1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9362542/v1/bbde4eb980ab6152a83b6418.xlsx"},{"id":108006213,"identity":"d45e82f6-e11b-476c-a079-7394c2ac48f9","added_by":"auto","created_at":"2026-04-28 12:54:37","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":19921,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-9362542/v1/a3952e787f4f26b619021414.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptome Analysis Reveals New Insights of Novel Duck Reovirus (NDRV)-Infected Duck Primary Macrophages","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDuck reovirus (DRV), a member of the genus Orthoreovirus in the family \u003cem\u003eReoviridae\u003c/em\u003e, is a common waterfowl virus with various morbidities [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. DRV was first identified in France (named Muscovy Duck Reovirus, MDRV), now has been found in many parts of the world, mainly in Europe and Asia [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. DRV can be phylogenetically classified into two distinct genotypes based on biological and genetic characteristics: genotype I (Classical DRV, CDRV) and II (Novel DRV, NDRV) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. To date, CDRV has been exclusively identified in Muscovy ducks and domestic geese, resulted in lameness, growth retardation, and immunosuppression, leading to increased susceptibility to secondary infections [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, a novel duck reovirus (NDRV), isolated from infected ducklings (including Pekin ducks, Cherry Valley ducks and geese), causes high mortality in ducklings but remains asymptomatic in adult ducks, has been frequently reported in southern China [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. First identified in 2005 in Fujian, Guangdong, and Zhejiang provinces, NDRV has since spread rapidly across China\u0026rsquo;s major duck farms, becoming endemic in these regions [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The morbidity and mortality rates range from 5%-35% and 2%-20%, respectively [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. NDRV infection induces spleen necrosis and bursal pathology, leading to immunosuppression and increased susceptibility to secondary bacterial infections [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Currently, there is still no effective vaccine available against NDRV infection. The DRV genome is composed of three large double-stranded RNA segments, three medium- sized segments, and four small segments. It encodes at least 10 structural proteins and 4 non- structural proteins with distinct electrophoretic mobilities [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Nevertheless, the regulatory mechanisms underlying ducklings' responses to NDRV infection remain largely elusive.\u003c/p\u003e \u003cp\u003eThe intricate interplay between viruses and hosts is pivotal for elucidating the mechanisms of viral pathogenesis. Transcriptomic and proteomic analyses have emerged as powerful and efficient tools for large-scale profiling of host gene and protein expression dynamics following viral infection. For example, two-dimensional polyacrylamide gel electrophoresis revealed 59 differentially expressed proteins in Muscovy duck embryo fibroblasts infected with MDRV, associated with carbohydrate and nucleotide metabolism, stress response, and immune regulation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. MDRV infection significantly up-regulated fatty acid degradation-related genes, including ATP binding cassette transport G8 and apolipoprotein A-IV, as well as chemotaxis cytokine receptor genes (CCR7, CCR9, CCR10) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The iTRAQ-LC-MS/MS analysis was employed to identify the protein profiles of NDRV-infected duck embryo fibroblasts (DEFs). At 24 h post-infection, 179 proteins (89 upregulated and 90 downregulated with a 1.5 - fold change cutoff) were found, NDRV infection activates the innate immune responses of the host cell, leading to the expression of various ISGs [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Yun et al. identified serine protease-associated proteins responding to C/NDRV infections in both liver and spleen using an advanced MS/MS-based approach. Furthermore, C/NDRV infections markedly increased expression of glycolysis-related enzymes, fructose bisphosphate aldolase and pyruvate kinase [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The spleen, which is rich in peripheral blood lymphocytes and macrophages, is the largest and most important peripheral immune organ in ducks. Previous studies have found that NDRV infection can cause harm to some organs of infected ducks, the viral load in the spleen and liver after NDRV infection was noticeably above those in the lung and heart [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Macrophages, as key phagocytic cells, play a pivotal role in host defense by engulfing and eliminating infectious agents, clearing cellular debris, and facilitating adaptive immunity through antigen presentation to T cells. These heterogeneous immune cells are ubiquitously distributed across all tissues and contribute to diverse physiological processes, including homeostasis, innate immune responses, tissue remodeling, and wound repair [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Their primary function, however, remains the orchestration of antimicrobial defense mechanisms to combat pathogenic invasions. Up to now, the role of macrophages in NDRV infection of healthy ducks remains unclear.\u003c/p\u003e \u003cp\u003eIn our research, in order to acquire transcriptomic information and discover the interactions of NDRV with host cells, duck macrophages were isolated and infected with NDRV, harvested at 12, 24 h post-infection (hpi), and subjected to transcriptomic sequencing. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were utilized to examine differentially expressed genes (DEGs) identified during different stages of infection. We hope our findings are served as a resource for improving the understanding of the effects of NDRV on immunosuppression in waterfowl and are of significant benefit to vaccine design and the development of antiviral drugs.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Cell culture and NDRV preparation\u003c/h2\u003e \u003cp\u003eDuck primary macrophages were isolated from the peripheral blood of healthy ducks using density gradient centrifugation. Briefly, blood was collected in heparinized tubes and diluted with phosphate-buffered saline (PBS). The diluted blood was layered over Ficoll-Paque PLUS (GE Healthcare) and centrifuged at 1500 rpm for 30 min at room temperature. The mononuclear cell layer was collected and washed twice with PBS. Cells were then resuspended in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS), 100 U/ml penicillin, and 100 \u0026micro;g/ml streptomycin. The cells were seeded in 6-well plates and incubated at 37\u0026deg;C in a 5% CO\u003csub\u003e2\u003c/sub\u003e humidified incubator for 2 h to allow macrophages to adhere. Non-adherent cells were removed by washing with PBS, and the adherent macrophages were cultured for further experiment.\u003c/p\u003e \u003cp\u003eThe NDRV originally isolated in Jiangsu Province in August 2008, was propagated in BHK-21 cells to a titer of 10\u003csup\u003e5.5\u003c/sup\u003eTCID\u003csub\u003e50\u003c/sub\u003e/0.1ml and maintained in our laboratory.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Virus Infection\u003c/h2\u003e \u003cp\u003eDuck primary macrophages (2\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells) were seeded in 6-well plates, grown to approximately 80%-90% confluency, and infected with NDRV at a multiplicity of infection (MOI) of 1. After 1 h of viral absorption at 37\u0026deg;C in a 5% CO\u003csub\u003e2\u003c/sub\u003e incubator, the inoculum was replaced with maintenance medium (DMEM plus 2% of FBS), and then harvested at 12 and 24 hpi. Uninfected cells were synchronized collected as a control. Each group was processed with three independent biological replicates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 RNA Extraction, library construction and sequencing\u003c/h2\u003e \u003cp\u003eTotal RNA was isolated from duck primary macrophages of each experimental group using the Trizol reagent kit (Invitrogen, Carlsbad, CA, USA) following the manufacturer\u0026rsquo;s instructions. RNA integrity and quality were evaluated using an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA) and further verified by RNase-free agarose gel electrophoresis. Following RNA extraction, eukaryotic mRNA was enriched using Oligo (dT) beads, whereas prokaryotic mRNA was enriched by depleting ribosomal RNA (rRNA) with the Ribo-Zero\u0026trade; Magnetic Kit (Epicenter, Madison, WI, USA). The enriched mRNA was subsequently fragmented into short segments using a fragmentation buffer and reverse-transcribed into complementary DNA (cDNA) using random primers. Second-strand cDNA synthesis was performed using DNA polymerase I, RNase H, and deoxynucleotide triphosphates (dNTPs). The resulting cDNA fragments were purified using the QiaQuick PCR extraction kit (Qiagen, Venlo, The Netherlands), followed by end repair, poly (A) tailing, and ligation to Illumina sequencing adapters. The ligated products were size-selected via agarose gel electrophoresis, amplified by PCR, and subjected to high-throughput sequencing on the Illumina HiSeq2500 platform (Gene Denovo Biotechnology Co., Guangzhou, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Transcriptomic Data Analyses\u003c/h2\u003e \u003cp\u003eTo ensure the acquisition of high-quality clean data, the RNA-Seq-derived datasets were subjected to stringent filtration utilizing fastp (version 0.18.0, accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/OpenGene/fastp\u003c/span\u003e\u003cspan address=\"https://github.com/OpenGene/fastp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Subsequent to this filtration, the resultant clean datasets were employed for downstream processes including transcriptome assembly and the quantification of gene abundance. The unigenes assembled from these datasets were aligned against the \u003cem\u003eAnas platyrhynchos\u003c/em\u003e (duck) reference genome (Ensembl_release110) employing HISAT2 version 2.2.4, with the parameter \"-rna-strandness RF\" alongside default settings. Alignment of reads for each specimen was conducted using StringTie version 1.3.1, adopting a reference-guided assembly strategy. Expression levels and variability of each transcriptional unit were quantified through the calculation of FPKM (Fragments Per Kilobase of transcript per Million mapped reads) values, facilitated by StringTie. Consequently, the differential expression analysis between NDRV-infected and mock-infected cohorts was executed and normalized utilizing the FPKM methodology, as delineated by Mortazavi et al., 2008 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Differential Expression Analysis\u003c/h2\u003e \u003cp\u003eDifferential expression analysis was conducted on data obtained from distinct groups at 12 and 24 hpi utilizing the DESeq2 software. Genes exhibiting a false discovery rate (FDR) of less than 0.05 and an absolute fold change of 2 or greater were identified as differentially expressed genes (DEGs). The \u003cem\u003ep\u003c/em\u003e-values were adjusted using the FDR method to control for multiple testing. To elucidate the biological functions of the DEGs, these genes were annotated against the Gene Ontology (GO) database (accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.geneontology.org/\u003c/span\u003e\u003cspan address=\"http://www.geneontology.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the frequency of genes associated with each GO term was quantified. This process yielded a catalog of genes annotated with specific GO functions and their corresponding counts. Subsequently, a hypergeometric distribution test was applied to identify GO categories significantly overrepresented among the DEGs relative to the entire genome. Furthermore, the Kyoto Encyclopedia of Genes and Genomes (KEGG) online annotation tool was employed to pinpoint pathways significantly enriched in the DEGs compared to the whole genome. For both GO and KEGG analyses, a corrected \u003cem\u003ep\u003c/em\u003e-value threshold of less than 0.05 was established to denote statistical significance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Immunofluorescence assay\u003c/h2\u003e \u003cp\u003eImmunofluorescence assay was performed as described previously [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Briefly, the cells were fixed and permeabilized by incubation in cold methanol at -20\u0026deg;C for 10 min. Following fixation, the cells were washed thoroughly and subsequently blocked with 1% bovine serum albumin (BSA) to minimize nonspecific binding. The cells were then incubated with appropriately diluted primary antibodies at 37\u0026deg;C for 2 h. After three washes with PBS, the cells were incubated with corresponding Alexa Fluor-conjugated secondary antibodies for 1 h at room temperature in the dark. Prepared Samples were observed under an inverted fluorescence microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Real-Time RT-PCR\u003c/h2\u003e \u003cp\u003eGene expression levels were quantified using quantitative PCR (qPCR) with the SYBR Premix Ex Taq\u0026trade; II Kit (Takara, China) on a Light Cycler 480 real-time PCR system (Roche). Specific primers for amplifying target genes identified by RNA-Seq were designed based on gene sequences retrieved from GenBank, utilizing Lasergene sequence analysis software (DNAStar, Inc., Madison, WI, USA; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The qPCR reactions were performed in a final volume of 20 \u0026micro;l, containing 0.8 \u0026micro;l of each primer and 1 \u0026micro;l of cDNA template. The thermal cycling protocol consisted of an initial activation step at 95\u0026deg;C for 30 s, followed by 40 cycles of denaturation at 95\u0026deg;C for 10 s, and annealing/extension at 60\u0026deg;C for 30 s. Melting curve analysis was conducted to verify amplification specificity, and relative gene expression levels were calculated using the 2\u003csup\u003e\u0026minus;ΔΔCT\u003c/sup\u003e method. Mock-infected HD11 cells served as the control group, with their expression levels normalized to 1. All reactions were performed in triplicate to ensure reproducibility.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.8 Statistical analyses\u003c/h2\u003e\n \u003cp\u003eData are expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean (SEM). Statistical differences between the control and treated groups were analyzed using the Student\u0026rsquo;s t-test. A p-value of less than 0.05 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was considered statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 NDRV replicated in Duck primary macrophages\u003c/h2\u003e\n \u003cp\u003eWe first determined whether duck primary monocyte-derived macrophages are permissive to NDRV infection \u003cem\u003ein vitro\u003c/em\u003e. Duck primary macrophages were infected with the virus for different time points at MOI\u0026thinsp;=\u0026thinsp;1. Infection and mock-infection were performed in biological triplicate for each time point and total RNA was extracted from both groups. From 12 hpi, a number of immunofluorescent cells could be detected after IFA staining using anti-NDRV antiserum (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No fluorescence was observed in mock-infected cells (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The replication of the viral genome was determined by RT-qPCR and the fold change, the results showed that the virus begins to replicate rapidly from 12 hpi (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To obtain an obviously changed transcriptome profile and minimize the influence of cell death and lysis, data at 12 hpi and 24 hpi were choosed and analyzed by RNA-Seq.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 RNA-Seq results\u003c/h2\u003e\n \u003cp\u003eFollowing a comprehensive quality assessment, mRNA was isolated from total RNA utilizing poly-T oligo-conjugated magnetic beads. Subsequently, a cDNA library was synthesized and subjected to rigorous quality evaluation. After cluster generation, the library preparations were sequenced on an Illumina HiSeqTM 2000 platform, yielding 150 bp paired-end reads. Each sequencing run generated a minimum of 40 million high-quality clean reads, with \u0026ge;\u0026thinsp;97% of the reads achieving a Q-score\u0026thinsp;\u0026gt;\u0026thinsp;20 and \u0026ge;\u0026thinsp;93% of the reads achieving a Q-score\u0026thinsp;\u0026gt;\u0026thinsp;30 (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Importantly, all samples had between 89.47% and 91.60% of total reads mapped to the duck reference genome. All of these results illustrated the high quality of the sequencing data and ensured their suitability for the next step of the analysis. The mapped data was normalized by calculating the FPKM and the distribution of mean FPKM per gene was found to be uniform. The correlation of gene expression levels between all of the samples was performed using the squared Pearson correlation coefficient (R\u003csup\u003e2\u003c/sup\u003e), revealed a minimum correlation value of 0.821 (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These results demonstrate that the expression levels of different genes or groups of genes are comparable, indicating that the treatment is repeatable and favorable.\u003c/p\u003e\n\u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Differentially expressed genes upon NDRV infection\u003c/h2\u003e \u003cp\u003eTo further investigate the differential expression patterns in duck primary macrophages between infected and mock-infected samples, the normalized gene expression level data were analyzed. Herein, the genome of \u003cem\u003eAnas platyrhynchos\u003c/em\u003e (duck) (Ensembl_release110) was utilized as our reference genome. The distinct effect on gene expression upon ARV infection was carefully analyzed under the threshold of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 \u0026amp; fold changes\u0026thinsp;\u0026ge;\u0026thinsp;2. Afterwards, the infected and mock-infected were compared with each other and the outline of the DEGs are listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Compared with mock infected controls, 3047 changes in the transcriptome, with 1834 up-regulated and 1213 down-regulated DEGs, were observed in response to NDRV infection at 12 hpi. 2514 changes were observed, with 1554 up-regulated and 960 down-regulated DEGs at 24 hpi. Compared with 12 hpi, 267 changes were observed, with 110 up-regulated and 157 down-regulated DEGs at 24 hpi. Also, these results were clearly visualized by clustering the samples by differential treatment and by constructing a volcano plot of the DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 GO annotation of DEGs upon NDRV infection\u003c/h2\u003e \u003cp\u003eAll the DEGs were classified into various gene ontology (GO) terms representing three major categories: \u0026lsquo;molecular function\u0026rsquo;, \u0026lsquo;cellular component\u0026rsquo; and \u0026lsquo;biological process\u0026rsquo;. Compared with mock infected controls, 28 GO terms in the biological process category, 3 GO terms in the cellular component category, and 19 GO terms in the molecular functions category were determined to be significantly enriched (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in response to NDRV infection at 12 hpi (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The significantly enriched \u0026lsquo;biological process\u0026rsquo; GO terms were associated with \u0026lsquo;cellular process\u0026rsquo;, \u0026lsquo;biological regulation\u0026rsquo;, \u0026lsquo;metabolic process\u0026rsquo;, \u0026lsquo;regulation of biological process\u0026rsquo;, \u0026lsquo;response to stimulus\u0026rsquo;, \u0026lsquo;mutlicellular organism process\u0026rsquo;, \u0026lsquo;signaling\u0026rsquo; and \u0026lsquo;localization\u0026rsquo;; within the \u0026lsquo;molecular function\u0026rsquo; category, the most represented GO terms were \u0026lsquo;binding\u0026rsquo;, \u0026lsquo;catalytic activity\u0026rsquo;, \u0026lsquo;molecular function regulator\u0026rsquo; and \u0026lsquo;transcription regulator activity\u0026rsquo;. Also, the most enriched GO terms within the \u0026lsquo;cellular component\u0026rsquo; category were \u0026lsquo;cellular anatomical entity\u0026rsquo; and \u0026lsquo;protein-containing complex\u0026rsquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCompared with mock infected controls, 28 GO terms in the biological process category, 3 GO terms in the cellular component category, and 17 GO terms in the molecular functions category were determined to be significantly enriched (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in response to NDRV infection at 24 hpi (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Furthermore, the differences in gene accumulation between 12 hpi and 24 hpi upon NDRV were compared, the most represented GO terms are similar to 12 hpi-vs-mock infected controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Pathway analysis of DEGs upon NDRV infection\u003c/h2\u003e \u003cp\u003eTo explore the various biological processes involved in NDRV infection, the differentially expressed genes were mapped into canonical signalling pathways using KEGG analysis. Under NDRV 12 hpi, the enriched DEGs were mainly involved in either disease pathways or host immune response, including cytokine-cytokine receptor interaction, TNF signaling pathway, viral protein interaction with cytokine and cytokine receptor, JAK-STAT signaling pathway, Toll-like receptor signaling pathway, Influenza A, Lipid and atherosclerosis, Kaposi sarcoma-associated herpesvirus infection, Human cytomegalovirus infection, pathway in cancer and NOD-like receptor signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Under NDRV 24 hpi, the KEGG terms were relatively similar to those at 12 hpi, albeit with slight differences in their order (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Collectively, these findings indicate that both viral agents and host cellular components employ distinct molecular strategies, which may contribute to the pathogenic mechanisms associated with NDRV infection.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Validation of RNA-Seq data by real-time qRT-PCR analysis\u003c/h2\u003e \u003cp\u003eTo validate the differential gene expression profiles obtained by RNA-Seq, expression of various genes involved in interferon stimulated gene (Mx1, IFIT5, OASL, CH25H), inflammatory response (TNF-α, PKR, IL1β, IL6, IL8, CCL4, CCL19, CCL20) and innate immunity (IFN-α, IFN-β, IFN-γ, MDA5, TLR7) were examined by qRT-PCR in HD11 cells. The data demonstrate that the overall results of qRT-PCR were consistent with those of the RNA-Seq (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Although several fold differences was observed between these two types of analysis because of intrinsic differences between the techniques and cell line. From the above results, it can be seen that the infiltrated macrophages, inflammatory cytokines including TNF-α, IL1β, IL6, and IL8, and emerging interactions of various cell populations through CCL4, CCL19, and CCL20, may contribute to the NDRV driven inflammatory spleen injury.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe emerging infectious disease caused by NDRV was first reported in several southern provinces of China in 2005, posing an enormous threat to the domestic poultry industry [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The disease caused by NDRV is an acute and contact infectious disease in duck farms, clinically characterized by splenic enlargement, necrotic lesions, and elevated mortality rates [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Information about the pathogenesis of NDRV and host-virus interactions has been poorly understood, though Yun identified a number of proteins responsive to the CDRV/NDRV infections by high throughput proteomics analysis using the control and infected spleen cells [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In the current study, we tried to build a complete expression profile of NDRV-mediated changes at the transcriptional level using RNA-Seq method to unveil the complex interactions between NDRV and host cells.\u003c/p\u003e \u003cp\u003eIn the previous study, RNA-Seq technology was applied to investigate the transcriptome-wide changes of DF-1 cells upon ARV infection at 10 hpi and 18 hpi [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. At 10 hpi, 104 genes were down-regulated and 64 were up-regulated, while 47 genes were up-regulated and only one was down-regulated at 18 hpi in the ARV-infected cells. These DEGs had functions in innate immunity, anti-stress and energy metabolism. In the present study, we used transcriptomic analysis to obtain global information on duck primary macrophages infected with NDRV at 12 hpi and 24 hpi. A total of 3047 DEGs were identified, with 1834 up-regulated and 1213 down-regulated DEGs were observed in response to NDRV infection at 12 hpi. 2514 changes were identified, with 1554 up-regulated and 960 down-regulated DEGs at 24 hpi. This indicates that, with the progression of technological advancements, an increasing number of DEGs can be identified, thereby elucidating the dynamic changes in host cells upon viral infection. The DEGs induced by NDRV infection were mainly involved in molecular function, cellular component, and biological process through the GO analysis. The signaling pathways of host innate immune system response and inflammatory response were observed significantly up-regulated according to the KEGG pathway analysis. The expression levels of selected DEGs within these pathways were validated using qPCR, and the results were consistent with the KEGG pathway analysis.\u003c/p\u003e \u003cp\u003eMacrophages are highly versatile immune cells that play pivotal roles in host defense, tissue homeostasis, and immune regulation. Derived from circulating monocytes, macrophages undergo context-dependent differentiation into distinct functional subsets, primarily classified as classically activated (M1) or alternatively activated (M2) macrophages [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This plasticity allows macrophages to adapt dynamically to microenvironmental cues, but it also renders them susceptible to exploitation by viral pathogens. Viruses have evolved sophisticated strategies to hijack macrophage machinery for their replication. For instance, human immunodeficiency virus (HIV) exploits CD4 and CCR5/CXCR4 co-receptors to infect macrophages, establishing persistent reservoirs that evade immune clearance [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Similarly, influenza A virus (IAV) engages sialic acid receptors to enter macrophages, where inefficient replication can trigger excessive cytokine production, contributing to immunopathology [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Macrophage tropism is also observed in dengue virus (DENV) infection, wherein antibody-dependent enhancement (ADE) facilitates viral entry, leading to enhanced replication and systemic inflammation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Importantly, many viruses manipulate macrophage polarization to favor their survival; for example, hepatitis C virus (HCV) promotes an M2-like phenotype to dampen antiviral responses [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Macrophages also play a key role in avian viral infections including avian influenza virus (AIV) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], Newcastle disease virus (NDV) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], infectious bursal disease virus (IBDV) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], infectious bronchitis virus (IBV) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and avian leukosis virus subgroup J (ALV-J) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, the role of macrophages in NDRV infection remains unclear.\u003c/p\u003e \u003cp\u003eIn the present study, the transcriptomic profiling elucidated an immune response diagram upon NDRV infection. Genes that were differentially expressed during NDRV infection can potentially provide insights into the complex regulatory phenomena. Initially, we found that NDRV infection activated many pattern recognition receptors (PRR) pathways including RIG-I-like receptors (RLR), NOD-like receptors (NLR) and Toll-like receptors (TLR). TLR7, an intracellular sensor of exogenous single-stranded RNA, is known to be modulated by various viruses including Tembusu virus (TMUV) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], ALV-J [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and H9N2 avian influenza virus [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Upon activation, TLR signaling pathways induce robust IFN production, playing a pivotal role in antiviral defense. In contrast to TLRs that are predominantly expressed in immune cells, RLRs are ubiquitously distributed in the cytoplasm of both immune and non-immune cells, functioning as independent viral sensors. RLRs mediate antiviral responses through viral RNA detection and subsequent activation of type I interferons and inflammatory cytokines. Furthermore, we tested MDA5, which also detects exogenous dsRNA, via RT-qPCR and found that it responded to NDRV, suggesting NDRV-derived dsRNA generated during viral replication is detected by these cytosolic sensors. NDRV were recognized by PRRs and captured by antigen-presenting cells (APCs), triggering the downstream signaling cascades and production of cytokines. IFN-α and IFN-γ, two types of antiviral cytokines, were up-regulated after viral infection, activating the JAK-STAT signaling pathways and inducing the transcription of several ISGs, such as Mx, PKR, CH25H, IFIT5 and OASL at 12 hpi and 24 hpi. ISGs exert broad antiviral effects by targeting diverse stages of the viral life cycle. For instance, Mx can prevent viral replication by trapping viral essential components and blocking the nuclear import of nucleocapsids or interacting with viral ribonucleoprotein structures [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. CH25H inhibits enveloped viruses (e.g., VSV, HSV, HIV, EBOV, RVFV, RSSEV) via cholesterol conversion to 25-hydroxycholesterol (25HC) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], while PKR, an IFN-induced dsRNA-dependent kinase, suppresses viral replication by phosphorylating EIF2α to block translation initiation [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These findings underscore the critical role of the interferon signaling pathway in restricting NDRV replication, consistent with its antiviral function against other viruses [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs we know, virus infections induce a proinflammatory response including expression of chemokines and cytokines [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In this study, increased expression of tumor necrosis factor-α (TNF-α), IL-1β, IL-6, IL-8, CCL20, CCL19, caspase 3, SOCS1 and SOCS3 were observed after NDRV infection. TNF-α is a pleiotropic proinflammatory cytokine primarily produced by activated macrophages, T lymphocytes, and NK cells during immune responses [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. As a pivotal mediator of innate immunity, TNF-α exerts its biological effects through binding to two distinct receptors: TNFR1 (p55) and TNFR2 (p75). In concert with chemokines, TNF and TNFRs are involved in the regulation of inflammatory processes like arthritis, of infectious diseases such as HIV infection, and of malignancies [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. CCL19 and CCL20 are chemokines belonging to the CC chemokine family which were detected in our transcriptome profiles. CCL19 mediates multiple immunological processes, including physiological lymphocyte recirculation, thymic T cell homing, and the migration of both T and B lymphocytes to secondary lymphoid organs [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Previous studies have indicated that NDRV induces excessive inflammatory responses and causes tissue damage in the spleen and liver [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In our study, a pro-inflammatory mediator (IL-6) and an anti-inflammatory mediator (IL-10) were significantly up-regulated upon NDRV infection. IL-6 is a pleiotropic cytokine with pivotal roles in immune regulation, cell growth, and differentiation [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Dysregulation of IL-6 and its receptor signaling contributes to the pathogenesis of various diseases, including immunorepressive disorders and malignancies [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Elevated IL-6 expression has been documented during infections with certain reoviruses and avian immunosuppressive viruses [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Inflammation in the spleen is known to be triggered by apoptotic processes, given the high abundance of peripheral blood lymphocytes and macrophages in this organ [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. We therefore hypothesized that NDRV infection induces splenocyte apoptosis, which in turn drives splenic inflammation, ultimately leading to tissue damage due to excessive inflammatory responses. As part of the innate immune defense against viral invasion, a robust production of cytokines and chemokines is elicited. Consistent with this, GO enrichment analysis revealed significant upregulation of cytokine- and chemokine-encoding genes during NDRV infection, suggesting their involvement in antiviral immunity.\u003c/p\u003e \u003cp\u003eInterestingly, we observed that expression of SOCS1 and SOCS3 was significantly up-regulated upon NDRV infection. SOCS-1 and SOCS-3, members of the STAT-induced STAT inhibitor (SSI) family (also termed suppressors of cytokine signaling), function as negative regulators of the JAK-STAT signaling pathway [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Our findings demonstrate their activation during infection, suggesting these molecules may protect the host from immunopathological damage through negative feedback regulation of excessive immune responses.\u003c/p\u003e \u003cp\u003eIn summary, our study provided valuable information regarding the transcriptome of macrophages against NDRV infection. Our results from transcriptomic analysis showed that DEGs related to innate immune response, including RLR, NLR signaling pathway, NF-κB signaling pathway, Jak-STAT signaling pathway and inflammation related pathway were participated in NDRV infection. Moreover, the expression profiles of inflammation related gene during NDRV infection were validated by qRT-PCR. Taken together, our data provided new insights into understanding the potential responses of immune-related genes against NDRV infection.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the open project of the key laboratory of prevention and control of major poultry diseases (Avian Influenza), Ministry of Agriculture and Rural Affairs (Grant No. YDWS202214).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHan K and Lu F conceived the study and edited the manuscript. Han K, Zhang L and Zhao D wrote the manuscript. Huang X, Wu F and Yang J prepared the figures and tables. Yin X, Su D and Liu Y helped to carry out the cell-culture and data analysis. Zhang X and Liu Q revised the manuscript. All authors approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCao Y, Sun M, Wang J, Hu X, He W, Su J (2019) Phenotypic and genetic characterisation of an emerging reovirus from Pekin ducks in China. Sci Rep 9 (1): 7784.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVarga-Kugler R, Marton S, Thuma A, Szentpali-Gavaller K, Balint A, Banyai K (2022) Candidate \u0026lsquo;avian orthoreovirus b\u0026rsquo;: An emerging waterfowl pathogen in europe and asia? \u0026zwnj;TransboundEmerg Dis 69 (5): e3386-e3392.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaudry D, Charles JM, Tektoff J (1972) A new disease expressing itself by a viral pericarditis in barbary ducks. C R Acad Hebd Seances Acad Sci D 274 (21): 2916\u0026ndash;2919.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYun T, Yu B, Ni Z, Ye W, Chen L, Hua J et al (2014) Genomic characteristics of a novel reovirus from muscovy duckling in china. Vet Microbiol 168 (2\u0026ndash;4): 261\u0026ndash;271.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen X, Zheng M, Lin F, Cheng X, Xiao S, Chen S et al (2019) Impacts of novel duck reovirus infection on the composition of intestinal microbiota of Muscovy ducklings. Microb Pathog 137: 103764.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Y, Liu Z, Zhu E, Li M, Jiang H, Luo Y et al (2019) Changes in the small intestine mucosal immune barrier in muscovy ducklings infected with muscovy duck reovirus. Vet Microbiol 233: 85\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang H, Gao B, Liu X, Zhang S, Diao Y, Tang Y (2020) Pathogenicity of a variant duck orthoreovirus strain in Cherry Valley ducklings. Vet Microbiol 242: 108546.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi N, Hong T, Wang Y, Wang Y, Yu K, Cai Y et al (2016) The pathogenicity of novel duck reovirus in Cherry Valley ducks. Vet Microbiol 192: 181\u0026ndash;185.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYun T, Hua J, Ye W, Yu B, Chen L, Ni Z et al (2018) Comparative proteomic analysis revealed complex responses to classical/novel duck reovirus infections in Cairna moschata. Sci Rep 8 (1): 10079.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKong J, Shao G, Zhang Y, Wang J, Xie Z, Feng K et al (2023) Molecular characterization, complete genome sequencing, and pathogenicity of novel duck reovirus from south coastal area in China. Poult Sci 102 (8): 102776.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang W, Liang J, Shi M, Chen G, Huang Y, Zhang Y et al (2020) The diagnosis and successful replication of a clinical case of duck spleen necrosis disease: An experimental co-infection of an emerging unique reovirus and salmonella indiana reveals the roles of each of the pathogens. Vet Microbiol 246: 108723.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo D, Liu R, Weng L, Li K, Qi X, Gao Y et al (2021) Genomic sequences and pathogenic characteristics of two variant duck reoviruses associated with spleen necrosis. Infect Genet Evol 92: 104847.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang Y, Zhang J, Dong J, Li L, Kuang R, Sun M et al (2022) Isolation and characterization of a new goose orthoreovirus causing liver and spleen focal necrosis in geese, China. Transbound Emerg Dis 69 (5): 3028\u0026ndash;3034.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenavente J, Mart\u0026iacute;nez-Costas J (2007) Avian reovirus: structure and biology. Virus Res 123:105\u0026ndash;119\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang M, Cheng X, Chen S, Zheng M, Chen S (2017) Analysis of differentially expressed proteins in Muscovy duck embryo fibroblasts infected with virulent and attenuated Muscovy duck reovirus by two-dimensional polyacrylamide gel electrophoresis. J Vet Med Sci 79 (12): 2063\u0026ndash;2069.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Y, Li L, Liu X, Jiang M, Zhao J, Li X et al (2020) Quantitative proteomic analysis of duck embryo fibroblasts infected with novel duck reovirus. Front Vet Sci 7: 577370.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYun T, Hua J, Ye W, Yu B, Ni Z, Chen L (2019) Comparative proteomic analysis revealed complex responses to classical/novel duck reovirus infections in the spleen tissue of Cairna moschata. J Proteomics 193: 162\u0026ndash;172.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWynn TA, Vannella KM (2016) Macrophages in Tissue Repair, Regeneration, and Fibrosis. Immunity 44 (3): 450\u0026ndash;462.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShapouri-Moghaddam A, Mohammadian S, Vazini H, Taghadosi M, Esmaeili SA, Mardani F et al (2018) Macrophage plasticity, polarization, and function in health and disease. J Cell Physiol 233 (9): 6425\u0026ndash;6440.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 50: 621\u0026ndash;628.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao P, Liu Y, Wang H, Chai Y, Weng W, Zhang Y et al (2022) Viral evasion of PKR restriction by reprogramming cellular stress granules. Proc Natl Acad Sci USA 119 (29): e2201169119.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiu X, Wang Y, Li M, Zhang X, Wu Y (2017) Transcriptome analysis of avian reovirus-mediated changes in gene expression of normal chicken fibroblast DF-1 cells. BMC Genomics 18 (1): 911\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeykers A, Leemans A, Gucht W, Schryver M, Cos P, Delputte P (2019) Differences in Susceptibility of Human and Mouse Macrophage Cell Lines to Respiratory Syncytial Virus Infection. Intervirology 62 (3\u0026ndash;4): 134\u0026ndash;144.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDecalf J, Desdouits M, Rodrigues V, Gobert F-X, Gentili M, Marques-Ladeira S et al (2017) Sensing of HIV-1 entry triggers a type I interferon response in human primary macrophages. J Virol 91 (15): e00147-17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuscitti C, Abinet J, Mar\u0026eacute;chal P, Meunier M, de mee\u0026ucirc;s C, Vanneste D (2024) Recruited atypical Ly6G+ macrophages license alveolar regeneration after lung injury. Sci Immunol 9(98):eado1227\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhanam A, Guti\u0026eacute;rrez-Barbosa H, Lyke K, Chua J (2022) Immune-mediated pathogenesis in dengue virus infection. Viruses 14 (11): 2575.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBility MT, Nio K, Li F, McGivern DR, Lemon SM, Feeney ER et al (2016) Chronic hepatitis C infection-induced liver fibrogenesis is associated with M2 macrophage activation. Sci Rep (6): 39520.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKawasaki T, Ikegawa M, Yunoki K, Otani H, Ori D, Ishii KJ et al (2022) Alveolar macrophages instruct CD8+ T cell expansion by antigen cross-presentation in lung. Cell Rep 41 (11): 111828.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang P, Ding Z, Liu X, Chen Y, Li J, Tao Z et al (2018) Enhanced replication of virulent Newcastle disease virus in chicken macrophages is due to polarized activation of cells by inhibition of TLR7. Front Immunol 9: 366.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu A, Li H, Qi X, Wang Q, Yang B, Wu T et al (2019) Macrophage migration inhibitory factor triggers inflammatory responses during very virulent infectious bursal disease virus infection. 10:2225 Front Microbiol\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan X, Tian Y, Guan R, Gao W, Yang X, Zhou L et al (2017) Infectious bronchitis virus infection induces apoptosis during replication in chicken macrophage HD11 cells. Viruses 9 (8): 198.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang K, Yang J, Zhou D, Zhu M, Du X, Zhou J et al (2022) Interaction of p10/p27 with macrophage migration inhibitory factor promotes avian leukosis virus subgroup J infection. Vet Microbiol 267: 109389.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe Y, Wang A, Chen S, Wu Z, Zhang J, Wang M et al (2017) Differential immune-related gene expression in the spleens of duck Tembusu virus-infected goslings. Vet Microbiol 212: 39\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen S, Wang A, Sun L, Liu F, Wang M, Jia R et al (2016) Immune-related gene expression patterns in GPV- or H9N2-infected goose spleens. Int J of Mol Sci 17 (12): 1990.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchneider WM, Chevillotte MD, Rice CM (2014) Interferon-stimulated genes: a complex web of host defenses. Annu Rev Immunol 32: 513\u0026ndash;545.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Zuo W, Zhang Y, Bo Z, Zhang C, Zhang X et al (2023) Cholesterol 25-hydroxylase suppresses avian reovirus replication by its enzymatic product 25-hydroxycholesterol. Front Microbiol 14: 1178005.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMogensen TH, Paludan SR (2001) Molecular pathways in virus-induced cytokine production. Microbiol Mol Boil Rev 65 (1): 131\u0026ndash;150.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSedger L, McDermott M (2014) TNF and TNF-receptors: From mediators of cell death and inflammation to therapeutic giants-past, present and future. Cytokine Growth Factor Rev 25 (4): 453\u0026ndash;472.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePasquereau S, Kumar A, Herbein G (2017) Targeting TNF and TNF receptor pathway in HIV-1 infection: from immune activation to viral reservoirs. Viruses 9 (4): 64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRangel-Moreno J, Moyron-Quiroz J, Kusser K, Hartson L, Nakano H, Randall TD (2005) Role of CXC chemokine ligand 13, CC chemokine ligand (CCL) 19, and CCL21 in the organization and function of nasal-associated lymphoid tissue. J Immunol 175: 4904\u0026ndash;4913.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoodman WA, Levine AD, Massari JV, Sugiyama H, McCormick TS, Cooper KD (2009) IL-6 signaling in psoriasis prevents immune suppression by regulatory T cells. J Immunol 183 (5): 3170\u0026ndash;3176.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu C, Chen M, Chen W, Hsieh C (2013) The role of IL-6 in the radiation response of prostate cancer. Radiat Oncol 8: 159.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiao J, Bao Y, Ye J, Shao H, Qian K, Qin A (2015) Transcriptional profiling of host gene expression in chicken embryo fibroblasts infected with reticuloendotheliosis virus strain HA1101. PLoS ONE 10 (5): e0126992.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang H, Jiang C, Xu B, Lei D, Fang R, Tang Y (2025) Transcriptomic analysis revealed ferroptosis in ducklings with splenic necrosis induced by NDRV infection. Vet Res 56 (1): 54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYasukawa H, Sasaki A, Yoshimura A (2000) Negative regulation of cytokine signaling pathways. Annu Rev Immunol 18: 143\u0026ndash;164.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"virus-genes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"viru","sideBox":"Learn more about [Virus Genes](http://link.springer.com/journal/11262)","snPcode":"11262","submissionUrl":"https://submission.nature.com/new-submission/11262/3","title":"Virus Genes","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"novel duck reovirus, duck primary macrophages, Transcriptome analysis","lastPublishedDoi":"10.21203/rs.3.rs-9362542/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9362542/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNovel duck reovirus (NDRV) is a newly identified reovirus that can cause hemorrhage and necrosis on the spleen of duck or goose, this disease has resulted in serious economic losses to the duck industry in China. Up to now, there is no effective vaccine against NDRV infection. To better understand the host cellular responses and the pathogenesis of NDRV infection, we performed transcriptomic profiling to compare gene expression changes between NDRV-infected and mock-infected duck primary macrophages. Duck primary macrophages were isolated and divided into three groups: mock-infected control, 12 hours post-infection (hpi), and 24 hpi. Differentially expressed genes (DEGs) in response to NDRV infection were identified using RNA sequencing (RNA-Seq). A total of 3047 DEGs were identified, including 1834 up-regulated and 1213 down-regulated genes at 12 hpi. In addition, a total of 2514 DEGs were identified at 24 hpi, including 1554 up-regulated and 960 down-regulated genes. Gene Ontology (GO) analysis showed that DEGs can be divided into the molecular function, cellular component and biological process. Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed that these DEGs were primarily involved in immune response, cellular metabolic processes, and signal transduction pathways, including TNF signaling, Toll-like receptor signaling, and JAK-STAT signaling pathways. Importantly, the expressions of inflammation-related genes and various interferon-stimulated genes (ISGs) were up-regulated after NDRV infection. Moreover, some selected DEGs were further examined by real-time PCR and the results were consistent with the RNA-Seq data. This study provides the first comprehensive transcriptomic profile of duck primary macrophages in response to NDRV infection, offering new insights into the molecular mechanisms of host-pathogen interactions and potential therapeutic targets for NDRV infection.\u003c/p\u003e","manuscriptTitle":"Transcriptome Analysis Reveals New Insights of Novel Duck Reovirus (NDRV)-Infected Duck Primary Macrophages","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-27 23:01:16","doi":"10.21203/rs.3.rs-9362542/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-04T21:45:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149082454767816974939247161874782025209","date":"2026-05-02T17:12:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-20T01:43:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-10T02:15:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-10T02:14:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Virus Genes","date":"2026-04-09T03:19:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"virus-genes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"viru","sideBox":"Learn more about [Virus Genes](http://link.springer.com/journal/11262)","snPcode":"11262","submissionUrl":"https://submission.nature.com/new-submission/11262/3","title":"Virus Genes","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2d68ebc1-017f-4366-8f7c-7349e1a44327","owner":[],"postedDate":"April 27th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-04T21:45:42+00:00","index":28,"fulltext":""},{"type":"reviewerAgreed","content":"149082454767816974939247161874782025209","date":"2026-05-02T17:12:00+00:00","index":25,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T23:01:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-27 23:01:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9362542","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9362542","identity":"rs-9362542","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}