Transcriptional Analysis Reveals Suppression of RAD51 and Disruption of the Homologous Recombination Pathway during PEDV Infection of IPEC-J2 Cells

Transcriptional Analysis Reveals Suppression of RAD51 and Disruption of the Homologous Recombination Pathway during PEDV Infection of IPEC-J2 Cells | 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 Transcriptional Analysis Reveals Suppression of RAD51 and Disruption of the Homologous Recombination Pathway during PEDV Infection of IPEC-J2 Cells Li Sun, Changfu Cao, Jianbo Yang, Jian Jin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5195612/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Dec, 2024 Read the published version in Virology Journal → Version 1 posted 11 You are reading this latest preprint version Abstract PEDV is a highly contagious enteric pathogen that can lead to severe diarrhea and death in neonatal pigs. Despite extensive research, the complete pathomechanism of PEDV and the molecular mechanisms of host responses remain unclear. In this study, differentially expressed genes (DEGs), time-specific co-expression modules, and key regulatory genes associated with PEDV infection were identified. Differential analysis showed that 2,275, 1,492, and 3,409 differential genes were screened in the 12 h vs. Mock, 24 h vs. Mock, and 48 h vs. Mock, respectively. Time series analysis showed that the genes of the up-regulated module were mainly involved in antiviral pathways such as viral defense response and regulation of immune system processes. Protein interaction network analysis revealed that the top 20 core genes in the interaction network included six up-regulated genes ( TFRC , SUOX , RMI1 , CD74 , IFIH1 , CD86 ) and 14 down-regulated genes ( FOS , CDC6 , CDCA3 , PIK3R2 , TUFM , VARS , ASF1B , POLD1 , MCM8 , POLA1 , CDC45 , BCS1L , RAD51 , RPA2 ). In addition, GSEA enrichment analysis showed that pathways such as DNA replication and homologous recombination were significantly inhibited during viral infection, and RAD51 , CDC6 , and RPA2 were involved. Our findings not only reveal dynamic changes in the transcriptome profile of PEDV-infected IPEC-J2 cells, but also provide novel insights into the mechanism of PEDV infection of the host. PEDV IPEC-J2 DEGs Transcriptome Molecular mechanism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Porcine epidemic diarrhea virus (PEDV) is a highly contagious enteric pathogen that induces severe diarrhea, dehydration, and high mortality rates in neonatal piglets [ 1 ]. PEDV, a member of the family Coronaviridae [ 2 , 3 ], is a single-stranded positive-sense RNA virus with a genome size of approximately 28 kb, comprising seven open reading frames that code for the ORF1a, ORF1b, S, ORF3, E, M, and N proteins [ 4 ]. Since the identification of the prototype strain in 1978 [ 3 ], PEDV infections have been reported annually across the globe, resulting in significant economic losses for the swine industry [ 5 – 9 ]. Despite extensive research, the complete pathological mechanisms of PEDV and the molecular mechanisms of host responses remain elusive. The genome's integrity is critical for cell survival and function, and DNA double-strand breaks (DSBs) pose one of the most serious threats to it [ 10 ]. Various factors, including endogenous damage like replication errors and exogenous factors like ionizing radiation and chemicals, can cause DSBs. To cope with these damages, cells have evolved a complex DNA repair mechanism, in which homologous recombination (HR) is one of the major pathways. HR relies on a highly conserved protein network that acts together to restore damaged DNA [ 11 ]. RAD51 is a core protein in HR, which is a homolog of bacterial RecA and plays a crucial role in eukaryotes [ 12 ]. By forming filamentous nucleoprotein structures, RAD51, an ATP-dependent DNA-binding protein, mediates homologous pairing and strand exchange reactions, enabling the repair of broken DNA fragments using their undamaged homologs as templates [ 13 ]. RAD51 's function is critical for maintaining genomic stability, and its defects can lead to chromosomal instability and increased cancer incidence [ 14 ]. Apart from its role in DNA repair, RAD51 is also associated with virus infection. Increasing evidence suggests that RAD51 is involved in the viral replication cycle and may influence the pathogenicity of viruses. For example, studies have shown that RAD51 can interact with HIV-1 integrase, inhibiting its activity and limiting HIV-1 replication [ 15 , 16 ]. RAD51 also plays a part in HBV infection by protecting the genome and helping to fix homologous DNA to support HBV replication [ 17 ]. In this study, we used comprehensive bioinformatics analysis processes, including differential expression analysis, time sequence clustering, weighted gene coexpression network analysis (WGCNA), and protein–protein interactions (PPI) network analysis, to investigate the transcription cluster time scenes of IPEC-J2 cells infected with PEDV at multiple time points (12 h, 24 h, and 48 h after infection) and identify the regulatory pathways and genes that play a critical role in hosts' replication and transmission of the virus. The findings indicate a significant inhibition of the homogenic reorganization pathway during the post-PEDV infection period, with RAD51 , a critical gene, playing a crucial role in this pathway. This suggests that PDEV could potentially disrupt the source reorganization pathway by suppressing RAD51 expression, thereby promoting virus reintegration and transmission. This study provides new insights into understanding the molecular mechanisms of PEDV infection and provides potential targets for developing treatment strategies for PEDV infection. In the future, we can delve deeper into the specific mechanisms of RAD51 's role in PEDV infection and investigate whether pharmacological interventions targeting RAD51 can effectively inhibit the replication and spread of PEPV. Materials and Methods Cells culture and virus infection IPEC-J2 cells were maintained in DMEM medium (Gibco, USA) with 10% fetal bovine serum (FBS, Gibco, USA) at 37°C and 5% CO 2 . This study used the PEDV CV777 strain, which was generously provided by China Agricultural University. Cells were infected with PEDV at a multiplicity of infection (MOI) of 1 and then cultured in DMEM medium containing 2 µg/mL trypsin at 37°C and 5% CO 2 for 1 h. After incubation, the cells were washed with phosphate-buffered saline and then cultured in DMEM medium containing 2% FBS. Detection of PEDV-M gene copy number Design fluorescent quantitative primers for the M gene based on the PEDV genome information, and detect the cycle number of the PEDV M gene through fluorescent quantitative analysis. Use the standard curve equation established in the early stage for PEDV-CV777 type: y = -3.3354lg(x) + 37.832, R2 = 0.9994, to calculate the copy number of PEDV. RNA extraction Samples collected at 12, 24, and 48 h post PEDV infection were subjected to RNA sequencing. IPEC-J2 cells were divided into four groups: cells infected with PEDV for 12 h, cells infected with PEDV for 24 h, cells infected with PEDV for 48 h, and control cells simulated for infection. Each group of cells had four biological replicates. According to the manufacturer's instructions, total RNA was extracted from PEDV-infected and uninfected cells using TRIzol® reagent (Invitrogen, USA). RNA quality and concentration were tested using NanoDrop 2000 (Thermo Scientific, USA) and Agilent 2100 Bioanalyzer (Agilent Technologies, USA). Samples with RNA Integrity Number (RIN) ≥ 7 were selected for further analysis. Library construction and sequencing Each sample used 1 µg of RNA as the input for RNA sequencing library preparation. Following the manufacturer's instructions, the Epicenter Ribo-Zero rRNA Removal Reagent was used to deplete ribosomal RNA, and the NEBNext® Ultra™ II Directional RNA Library Prep Kit for Illumina® was used to generate mRNA sequencing libraries. The purity of the library products was then evaluated using the Agilent 2100 Bioanalyzer, and sequencing was performed on the Illumina HiSeq X Ten platform by OE Biotech Co. Analysis of differentially expressed mRNAs Differential expression gene analysis was performed between the groups (12 h vs Mock, 24 h vs Mock, 48 h vs Mock, 24 h vs 12 h, 48 h vs 12 h, 48 h vs 24 h) using the R package DESeq2 (version: 1.34.0) [ 18 ], and genes with p-adj 1 were selected as differential genes. Enrichment analysis Functional enrichment analysis of the genes was performed based on the Gene Ontology (GO) [ 19 , 20 ], Kyoto Encyclopedia of Genes and Genomes (KEGG) [ 21 , 22 ], and Reactome [ 23 ] databases. The hypergeometric distribution test was used with the enrichGO and enrichKEGG functions from the clusterProfiler R package (version 4.2.2) [ 24 ] to identify enriched pathways from the GO and KEGG databases. Additionally, the enrichPathway function from the ReactomePA package was used to perform enrichment analysis on the Reactome database. Pathways with p-values less than 0.05 were retained for further analysis. Time-series Analysis Soft clustering analysis was performed using the fuzzy c-means algorithm provided by the Mfuzz R package (version 2.54.0) [ 25 ] to identify different expression patterns of genes in the time-series experimental design. Two parameters, c (number of clusters) and m (fuzziness parameter), were required for this analysis. The value of parameter c was determined by evaluating the change in the sum of squared errors as the number of clusters increased, and the value of parameter m was obtained using the mestimate function from the Mfuzz package. After determining the two key parameters, clustering was performed, and genes with a membership degree > 0.6 were retained to ensure similar expression trends within each group. Construction of Weighted Gene Co-expression Network Weighted gene co-expression network analysis [ 26 ] was used to construct gene co-expression modules from the gene expression profiles. First, a gene relationship matrix was obtained from the gene expression profiles using Pearson correlation coefficients. By setting the soft threshold β to 14, the gene relationship matrix derived from Pearson correlation coefficients was transformed into an adjacency matrix. Then, the topological overlap matrix (TOM) was calculated to measure the interconnectivity of the network. We used the dissimilarity of TOM as the clustering distance to divide the genes into different modules. Additionally, a dynamic tree-cutting algorithm was applied, with a threshold of 0.25, to merge similar gene modules. Protein-Protein Interaction Network Analysis Protein-protein interaction information for the corresponding genes was retrieved from the STRING database (version 12.0) [ 27 ]. A minimum interaction confidence score threshold of 0.4 was set, retaining only interactions with a confidence score greater than or equal to 0.4. The protein-protein interaction network was constructed using Cytoscape software. Topological analysis of the network was performed, calculating node-specific metrics such as degree and betweenness centrality. Quantitative real time‑PCR (qPCR) verification Based on the gene sequences published in the GenBank database, qPCR primers were designed using Primer Premier 5.0 software, with GAPDH as the reference gene. All primers were synthesized by Sangon Biotech (Shanghai) Co., Ltd. Real-time quantitative PCR (qPCR) analysis was performed using a real-time fluorescence detection kit. All qPCR reactions were carried out in a 20 µL volume, with 10 µL of 2× SYBR Premix ExTapTM II, 0.4 µL of 10 µmol/L PCR Forward Primer, 0.4 µL of 10 µmol/L PCR Reverse Primer, 0.4 µL of 50× ROX Reference Dye II, 2.0 µL of cDNA, and RNase-free dH2O to a total volume of 20 µL. Three independent experimental replicates were set up for each sample. The qPCR amplification program was as follows: 95°C for 5 min; 95°C for 10 s, 60°C for 30 s, for a total of 40 cycles. To analyze the specificity of the amplification products, multiple data points were collected after the PCR amplification, and melting curve analysis was performed. Statistical Analysis and Data Visualization All statistical analysis is done in R environment and the visualization of the data is done using the R package ggplot2. Results Differential transcriptomic landscapes delineate PEDV infection dynamics at multiple time points Normal IPEC-J2 cells exhibited irregular shapes with clear outlines and distinct boundaries, evenly distributed on the cell plate. After infection with the classical PEDV strain CV777, the cells showed obvious shrinkage, became rounded, and lost their normal cellular morphology, displaying typical cytopathic effects (Fig. 1 A). qPCR results revealed that the copy number of the PEDV-M gene gradually increased at 12 h and 24 h post-infection, reaching the highest expression level at 24 h, and slightly decreased at 48h (Fig. 1 B). Subsequently, transcriptome sequencing was performed on samples from different infection time points. The boxplot results demonstrated relatively uniform distribution of TPM values for all sample genes (Fig. 2 A). PCA results indicated that the genes within each group exhibited similar expression patterns, while the samples between groups could be well distinguished (Fig. 2 B). These results suggested that the sequencing data quality was satisfactory for further bioinformatics analysis. Differential analysis revealed 2,275 (611 upregulated and 1,664 downregulated), 1,492 (609 upregulated and 883 downregulated), 3,409 (2,093 upregulated and 1,316 downregulated), 2,231 (1,509 upregulated and 722 downregulated), 5,417 (3,398 upregulated and 2,019 downregulated), and 2,703 (1,951 upregulated and 752 downregulated) DEGs in the 12 h vs Mock, 24 h vs Mock, 48 h vs Mock, 24 h vs 1 2h, 48 h vs 12 h, and 48 h vs 24 h comparison groups, respectively (Fig. 2 C, D). Enrichment analysis showed that pathways such as the JAK-STAT signaling pathway, MAPK signaling pathway, cytokine-cytokine receptor interaction, and PI3K-Akt signaling pathway were enriched in the DEGs of the Mock group. Immune-related pathways, including regulation of T cell apoptotic process, regulation of lymphocyte differentiation, and regulation of adaptive immune response, were only enriched in the DEGs of the 48 h vs Mock group. Pathways such as metabolism of carbohydrates, HIF-1 signaling pathway, and positive regulation of lipid transport were exclusively enriched in the 12 h vs Mock group (Fig. 3 ). Time-course transcriptomics uncvers transcriptional reprogramming in PEDV infection To investigate the gene expression trends across different groups, this study employed a soft-threshold clustering method based on the within-group sum of squares "gap" statistic (Fig. 4 A) and classified the gene expression patterns into 5 clusters (Fig. 4 C and Table S1 ). The gene expression heatmap displayed the expression patterns of genes from Cluster 1 to Cluster 5 (Fig. 4 B). Genes in Cluster 1 exhibited an upward trend in expression after PEDV infection, and enrichment analysis revealed that these genes were mainly involved in antiviral pathways such as defense response to virus, regulation of immune system process, and TNF signaling pathway. Conversely, genes in Cluster 3 and Cluster 4 showed a downward trend in expression following PEDV infection and were primarily enriched in metabolism-related pathways, including cellular amide metabolic process, iposaccharide metabolic process, and carbohydrate derivative metabolic process. Furthermore, Cluster 2 and Cluster 5 displayed irregular expression patterns over time and were mainly enriched in pathways such as RNA splicing, rRNA processing, cell cycle, and tight junction (Fig. 4 D; Fig. 5 A, B and Table S2, 3). WGCNA identifies time-specific co-expression modules in PEDV infection The study utilized WGCNA analysis to investigate gene co-expression networks associated with the PEDV infection process. The sample clustering tree revealed no outlier samples, indicating that all samples could be further analyzed using WGCNA (Fig. 6 A). By setting the soft threshold β to 14 (Fig. 6 B), WGCNA successfully divided the genes into eight co-expression modules represented by different colors (Fig. 6 C), with each module exhibiting distinct expression patterns (Fig. 6 D). Among these modules, the brown gene module (r=-0.64, p = 8e-03), cyan gene module (r = 0.76, p = 7e-04), darkred gene module (r=-0.85, p = 3e-05), and darkgrey gene module (r = 0.72, p = 2e-03) showed significant correlations with the PEDV infection process (Fig. 6 E). To further explore the functions of the gene modules significantly associated with the PEDV infection process, we performed enrichment analysis on the key genes (MM ≥ 0.8, GS ≥ 0.8) within the brown, cyan, darkred, and darkgrey gene modules (Fig. 6 F). GO enrichment analysis revealed that these genes were primarily involved in biological pathways such as T cell proliferation, positive regulation of cell-cell adhesion, and regulation of cell population proliferation (Fig. 7 A). KEGG enrichment analysis showed enrichment in pathways including TNF signaling pathway, viral protein interaction with cytokine and cytokine receptor, Toll-like receptor signaling pathway, and JAK-STAT signaling pathway (Fig. 7 B). Reactome enrichment analysis indicated enrichment in biological pathways such as extracellular matrix organization, homology-directed repair, and KK complex recruitment mediated by RIP1 (Fig. 7 C). PPI network analysis uncovers regulatory hubs in PEDV infection To identify key core regulatory factors during the PEDV infection process, this study conducted protein interaction network analysis and found that among the 985 key genes associated with PEDV infection, the top 20 core genes in the interaction network included 6 upregulated genes ( TFRC , SUOX , RMI1 , CD74 , IFIH1 , and CD86 ) and 14 downregulated genes ( FOS , CDC6 , CDCA3 , PIK3R2 , TUFM , VARS , ASF1B , POLD1 , MCM8 , POLA1 , CDC45 , BCS1L , RAD51 , and RPA2 ) (Fig. 8 A, B). GSEA enrichment analysis of the 48 h vs Mock group revealed that among the 20 core genes, only RAD51 , CDC6 , and RPA2 were enriched, primarily participating in pathways such as DNA replication and homologous recombination (Fig. 8 C, D). Discussion PEDV has resulted in substantial economic losses within the livestock industry, and existing commercial vaccines have not been able to offer complete protection for pigs. Despite advancements in PEDV research over recent decades, a comprehensive understanding of the virus's pathogenic mechanisms remains elusive. This investigation utilized bioinformatics analysis to examine the transcriptional alterations in PEDV-infected IPEC-J2 cells across various time intervals, identifying DEGs, time-specific co-expression modules, and crucial regulatory elements linked to PEDV infection. Through WGCNA analysis, gene co-expression modules significantly associated with the progression of PEDV infection were identified. These modules play pivotal roles in the immune response and metabolic regulation of host cells during PEDV infection. The findings of this study indicate that host cell immune responses are triggered following PEDV infection, resulting in the proliferation of immune cells and the modulation of immune-related pathways. The innate immune system acts as the primary defense against viral infections, with pattern recognition receptors (PRRs) identifying viral-associated molecular patterns, initiating downstream signaling pathways, and generating antiviral substances like interferon [ 28 , 29 ]. However, viruses have developed various strategies to evade innate immunity, including inhibiting PRR recognition, disrupting interferon signaling pathways, and suppressing the expression of antiviral genes [ 30 , 31 ]. Additionally, PEDV may manipulate the metabolic processes of host cells to create a conducive environment for its replication and survival. The interaction between PEDV and host cells also influences cellular functions such as RNA splicing [ 32 ], cell cycle regulation [ 33 ], and cell-cell connections [ 34 ]. One significant discovery is the notable inhibition of the homologous recombination pathway following PEDV infection. HR is a DNA repair mechanism essential for maintaining genome stability and integrity [ 35 ]. RAD51, a core protein of HR responsible for homologous pairing and strand exchange reactions to ensure accurate repair of DSBs [ 36 ], is downregulated in PEDV-infected IPEC-J2 cells. This indicates that PEDV may disrupt the HR pathway, potentially facilitating viral replication and spread by suppressing RAD51 expression. Decreased RAD51 levels have been linked to increased chromosomal instability and cancer risk. In viral infections, RAD51 is associated with the replication cycle and pathogenicity of various viruses [ 37 , 38 ]. For instance, RAD51 interacts with the replication initiator protein of Begomovirus to enhance viral replication [ 39 ]. In astrocytes, elevated RAD51 levels boost HIV-1 LTR activity in conjunction with HIV-1 Tat, transcription factor C/EBPβ, and CHOP [ 40 ]. Knocking down RAD51 significantly reduces HCV protein expression and intracellular and extracellular HCV RNA levels pre-HCV infection [ 37 ]. Through protein-protein interaction network analysis, this study identified key regulatory factors of PEDV infection. The top 20 genes in the network, including RAD51 , CDC6 , and RPA2 , are involved in DNA replication, homologous recombination, and other pathways. This underscores the potential roles of these genes in the viral replication cycle and suggests they could be targeted to inhibit PEDV replication and dissemination. Conclusions In summary, our study provides insights into the molecular mechanisms of PEDV infection and identifies potential targets for developing therapeutic strategies against PEDV infection. PEDV inhibits the homologous recombination pathway by downregulating the expression of RAD51 , which may promote viral replication and dissemination. Furthermore, the dynamic changes in gene expression patterns, involvement of immune-related pathways, and regulation of host cellular processes highlight the complex interaction between PEDV and host cells. Abbreviations PEDV Porcine epidemic diarrhea virus DEGs Differentially expressed genes DSBs DNA double-strand breaks HR Homologous recombination WGCNA Weighted gene coexpression network analysis PPI Protein–protein interactions IPEC-J2 Porcine small intestinal epithelial cells TOM The topological overlap matrix PRRs Pattern recognition receptors Declarations Acknowledgments Not applicable. Author contributions J.J. conceived and designed the experiments. L.S. and C.F.C. performed the experiments. L.S. and J.B.Y. analyzed the data. J.J. and L.S. wrote the paper. All authors read and approved the final version of the paper. Funding This research was funded by Youth Support Project of Jiangsu Vocational College of Agriculture and Forestry (No. 2021kj18); Basic Science (Natural Science) Research Project of Colleges in Jiangsu Province (No. 21KJB230003). Data availability All data needed to evaluate the conclusions in the paper are available in the Genome Sequence Archive (GSA) (CRA013464) maintained by the Beijing Institute of Genomics (BIG) Data Center. The names of the repository/repositories and accession number(s) can be found below: https://bigd.big.ac.cn/gsa, CRA019128. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no conflicts of interest. References Jung K, Annamalai T, Lu Z, Saif LJ. Comparative pathogenesis of US porcine epidemic diarrhea virus (PEDV) strain PC21A in conventional 9-day-old nursing piglets vs. 26-day-old weaned pigs. Vet Microbiol. 2015;178:31–40. 10.1016/j.vetmic.2015.04.022 . Cavanagh D. Nidovirales: a new order comprising Coronaviridae and Arteriviridae. Arch Virol. 1997;142:629–33. Pensaert MB, de Bouck P. A new coronavirus-like particle associated with diarrhea in swine. Arch Virol. 1978;58:243–7. 10.1007/bf01317606 . Cheun-Arom T, Temeeyasen G, Tripipat T, Kaewprommal P, Piriyapongsa J, Sukrong S, Chongcharoen W, Tantituvanont A, Nilubol D. Full-length genome analysis of two genetically distinct variants of porcine epidemic diarrhea virus in Thailand. Infect Genet Evol. 2016;44:114–21. 10.1016/j.meegid.2016.06.046 . Davies PR. The dilemma of rare events: Porcine epidemic diarrhea virus in North America. Prev Vet Med. 2015;122:235–41. 10.1016/j.prevetmed.2015.08.006 . Jung K, Saif LJ. Porcine epidemic diarrhea virus infection: Etiology, epidemiology, pathogenesis and immunoprophylaxis. Vet J. 2015;204:134–43. 10.1016/j.tvjl.2015.02.017 . Wang D, Fang L, Xiao S. Porcine epidemic diarrhea in China. Virus Res. 2016;226:7–13. 10.1016/j.virusres.2016.05.026 . Wang Q, Vlasova AN, Kenney SP, Saif LJ. Emerging and re-emerging coronaviruses in pigs. Curr Opin Virol. 2019;34:39–49. 10.1016/j.coviro.2018.12.001 . Wood EN. An apparently new syndrome of porcine epidemic diarrhoea. Vet Rec. 1977;100:243–4. 10.1136/vr.100.12.243 . Li H, Zimmerman SE, Weyemi U. Genomic instability and metabolism in cancer. Int Rev Cell Mol Biol. 2021;364:241–65. 10.1016/bs.ircmb.2021.05.004 . Moynahan ME, Jasin M. Mitotic homologous recombination maintains genomic stability and suppresses tumorigenesis. Nat Rev Mol Cell Biol. 2010;11:196–207. 10.1038/nrm2851 . Benson FE, Stasiak A, West SC. Purification and characterization of the human Rad51 protein, an analogue of E. coli RecA. Embo j. 1994;13:5764–71. 10.1002/j.1460-2075.1994.tb06914.x . Vispé S, Defais M. Mammalian Rad51 protein: a RecA homologue with pleiotropic functions. Biochimie. 1997;79:587–92. 10.1016/s0300-9084(97)82007-x . Dietlein F, Thelen L, Reinhardt HC. Cancer-specific defects in DNA repair pathways as targets for personalized therapeutic approaches. Trends Genet. 2014;30:326–39. 10.1016/j.tig.2014.06.003 . Cosnefroy O, Tocco A, Lesbats P, Thierry S, Calmels C, Wiktorowicz T, Reigadas S, Kwon Y, De Cian A, Desfarges S, et al. Stimulation of the human RAD51 nucleofilament restricts HIV-1 integration in vitro and in infected cells. J Virol. 2012;86:513–26. 10.1128/jvi.05425-11 . Thierry S, Benleulmi MS, Sinzelle L, Thierry E, Calmels C, Chaignepain S, Waffo-Teguo P, Merillon JM, Budke B, Pasquet JM, et al. Dual and Opposite Effects of hRAD51 Chemical Modulation on HIV-1 Integration. Chem Biol. 2015;22:712–23. 10.1016/j.chembiol.2015.04.020 . Pasaje CF, Kim JH, Park BL, Cheong HS, Bae JS, Park TJ, Lee JS, Kim Y, Lee HS, Koh I, et al. Lack of association of RAD51 genetic variations with hepatitis B virus clearance and occurrence of hepatocellular carcinoma in a Korean population. J Med Virol. 2011;83:1892–9. 10.1002/jmv.22122 . Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550. 10.1186/s13059-014-0550-8 . Aleksander SA, Balhoff J, Carbon S, Cherry JM, Drabkin HJ, Ebert D, Feuermann M, Gaudet P, Harris NL, Hill DP, et al. The Gene Ontology knowledgebase in 2023. Genetics. 2023;224. 10.1093/genetics/iyad031 . Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25–9. 10.1038/75556 . Kanehisa M, Furumichi M, Sato Y, Kawashima M, Ishiguro-Watanabe M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2023;51:D587–92. 10.1093/nar/gkac963 . Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30. 10.1093/nar/28.1.27 . Milacic M, Beavers D, Conley P, Gong C, Gillespie M, Griss J, Haw R, Jassal B, Matthews L, May B, et al. The Reactome Pathway Knowledgebase 2024. Nucleic Acids Res. 2024;52:D672–8. 10.1093/nar/gkad1025 . Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innov (Camb). 2021;2:100141. 10.1016/j.xinn.2021.100141 . Kumar L, M EF. Mfuzz: a software package for soft clustering of microarray data. Bioinformation. 2007;2:5–7. 10.6026/97320630002005 . Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559. 10.1186/1471-2105-9-559 . Szklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R, Gable AL, Fang T, Doncheva NT, Pyysalo S, et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023;51:D638–46. 10.1093/nar/gkac1000 . Takeuchi O, Akira S. Pattern recognition receptors and inflammation. Cell. 2010;140:805–20. 10.1016/j.cell.2010.01.022 . Yatim KM, Lakkis FG. A brief journey through the immune system. Clin J Am Soc Nephrol. 2015;10:1274–81. 10.2215/cjn.10031014 . Lin F, Zhang H, Li L, Yang Y, Zou X, Chen J, Tang XPEDV. Insights and Advances into Types, Function, Structure, and Receptor Recognition. Viruses. 2022;14. 10.3390/v14081744 . Zhao J, Qin C, Liu Y, Rao Y, Feng P. Herpes Simplex Virus and Pattern Recognition Receptors: An Arms Race. Front Immunol. 2020;11:613799. 10.3389/fimmu.2020.613799 . Boudreault S, Roy P, Lemay G, Bisaillon M. Viral modulation of cellular RNA alternative splicing: A new key player in virus-host interactions? Wiley Interdiscip Rev RNA. 2019;10:e1543. 10.1002/wrna.1543 . Salamango DJ, Harris RS. Demystifying Cell Cycle Arrest by HIV-1 Vif. Trends Microbiol. 2021;29:381–4. 10.1016/j.tim.2021.01.001 . Vaswani CM, Varkouhi AK, Gupta S, Ektesabi AM, Tsoporis JN, Yousef S, Plant PJ, da Silva AL, Cen Y, Tseng YC, et al. Preventing occludin tight-junction disruption via inhibition of microRNA-193b-5p attenuates viral load and influenza-induced lung injury. Mol Ther. 2023;31:2681–701. 10.1016/j.ymthe.2023.06.011 . Ceccaldi R, Liu JC, Amunugama R, Hajdu I, Primack B, Petalcorin MI, O'Connor KW, Konstantinopoulos PA, Elledge SJ, Boulton SJ, et al. Homologous-recombination-deficient tumours are dependent on Polθ-mediated repair. Nature. 2015;518:258–62. 10.1038/nature14184 . Bonilla B, Hengel SR, Grundy MK, Bernstein KA. RAD51 Gene Family Structure and Function. Annu Rev Genet. 2020;54:25–46. 10.1146/annurev-genet-021920-092410 . Son K, Nguyen TTT, Choi JW, Pham LV, Luong TTD, Lim YS, Hwang SB. Rad51 Interacts with Non-structural 3 Protein of Hepatitis C Virus and Regulates Viral Production. Front Microbiol. 2017;8:1249. 10.3389/fmicb.2017.01249 . Tang KW, Norberg P, Holmudden M, Elias P, Liljeqvist J. Rad51 and Rad52 are involved in homologous recombination of replicating herpes simplex virus DNA. PLoS ONE. 2014;9:e111584. 10.1371/journal.pone.0111584 . Suyal G, Mukherjee SK, Choudhury NR. The host factor RAD51 is involved in mungbean yellow mosaic India virus (MYMIV) DNA replication. Arch Virol. 2013;158:1931–41. 10.1007/s00705-013-1675-x . Chipitsyna G, Sawaya BE, Khalili K, Amini S. Cooperativity between Rad51 and C/EBP family transcription factors modulates basal and Tat-induced activation of the HIV-1 LTR in astrocytes. J Cell Physiol. 2006;207:605–13. 10.1002/jcp.20612 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.zip Cite Share Download PDF Status: Published Journal Publication published 28 Dec, 2024 Read the published version in Virology Journal → Version 1 posted Editorial decision: Revision requested 12 Nov, 2024 Reviews received at journal 06 Nov, 2024 Reviews received at journal 31 Oct, 2024 Reviews received at journal 19 Oct, 2024 Reviewers agreed at journal 13 Oct, 2024 Reviewers agreed at journal 13 Oct, 2024 Reviewers agreed at journal 10 Oct, 2024 Reviewers invited by journal 08 Oct, 2024 Editor assigned by journal 07 Oct, 2024 Submission checks completed at journal 07 Oct, 2024 First submitted to journal 02 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-5195612","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":377265616,"identity":"f4ba4b76-d1cb-47e3-aa85-df4a1c13cdac","order_by":0,"name":"Li Sun","email":"","orcid":"","institution":"Jiangsu Vocational College of Agriculture and Forestry","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Sun","suffix":""},{"id":377265617,"identity":"95f7ae98-f008-4915-8095-61643a8c87d9","order_by":1,"name":"Changfu Cao","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Changfu","middleName":"","lastName":"Cao","suffix":""},{"id":377265618,"identity":"00a55c35-bb01-429a-8b98-85fe95d8bbaa","order_by":2,"name":"Jianbo Yang","email":"","orcid":"","institution":"Jiangsu Vocational College of Agriculture and Forestry","correspondingAuthor":false,"prefix":"","firstName":"Jianbo","middleName":"","lastName":"Yang","suffix":""},{"id":377265619,"identity":"bad5f8d1-eeab-4df9-96e9-92514b21ec4e","order_by":3,"name":"Jian Jin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYDCCA4yNDxIqJHjYmJkPPoAIJRDSwtxs8OGMhRw/e1uyAZFa2NskZ7ZVGEv2nDGTIEoL3/mDDdI8bBKJG26kpVXz/DnMwM+eY8DwcwduLZIHDjYY8/CAtCQfu83Dc5hBsueNAWPvGdxaDA42NiTzSEBsuc0jcZjB4EaOATNjGx4thxkbDvMYgLTkmBXzGBxmsCeo5RhjY+OMBAmw95l5EoC2SBDQInmGsZnhwwEJcCBLzjmQziNx5lnBwV48WvjOH3/+I/FfHTgqP7z5Yy3H35688cFPPFpQABMPAwMPiHGASA0MDIw/iFY6CkbBKBgFIwkAAEYPVruIBhDCAAAAAElFTkSuQmCC","orcid":"","institution":"Yangzhou University","correspondingAuthor":true,"prefix":"","firstName":"Jian","middleName":"","lastName":"Jin","suffix":""}],"badges":[],"createdAt":"2024-10-03 03:38:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5195612/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5195612/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12985-024-02611-8","type":"published","date":"2024-12-28T15:57:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70287272,"identity":"c2a774c1-30ac-44fe-b13d-4433a60f1614","added_by":"auto","created_at":"2024-12-01 16:51:06","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5206889,"visible":true,"origin":"","legend":"\u003cp\u003ePEDV infection induces damage to IPEC-J2 cells at different time points. (\u003cstrong\u003eA\u003c/strong\u003e) Observation under an optical microscope. (\u003cstrong\u003eB\u003c/strong\u003e) PEDV copy numbers in IPEC-J2 cells at different time points post-infection.\u003c/p\u003e","description":"","filename":"Figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5195612/v1/0e8400150a42f1fc18b2cbb1.jpg"},{"id":70287814,"identity":"e3f197f8-a1e7-4615-b640-da3011bd5732","added_by":"auto","created_at":"2024-12-01 16:59:06","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2130866,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of differentially expressed genes. (\u003cstrong\u003eA\u003c/strong\u003e) Box plot of log2(TPM) values for mRNA across different time points. (\u003cstrong\u003eB\u003c/strong\u003e) PCA diagram on normalized mRNA expression values illumi-nating the general relationship between datasets. (\u003cstrong\u003eC\u003c/strong\u003e) Differential gene expression results dis-playing up‐ and down‐regulated genes between four comparison groups (12 h vs Mock, 24 h vs Mock, 48 h vs Mock, 24 h vs 12 h, 48 h vs 12 h and 48 h vs 24 h). The threshold for differential expression genes is |log2(FC)| \u0026gt;1 and adjust pvalue \u0026lt;0.05. Blue dots indicate down‐regulated genes, and red dots indicate up‐regulated genes. (\u003cstrong\u003eD\u003c/strong\u003e) Histogram of the number of differential expression genes.\u003c/p\u003e","description":"","filename":"Figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5195612/v1/da92141a0c3accfb2d49972b.jpg"},{"id":70287269,"identity":"2aa208b7-9ef0-4547-a0cb-b380368c7f92","added_by":"auto","created_at":"2024-12-01 16:51:06","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2445480,"visible":true,"origin":"","legend":"\u003cp\u003eGO, KEGG, and REACTOME enrichment analyses. The y-axis represents pathway entries, and the x-axis represents the grouping of differentially expressed genes. The shape of the plot represents different databases, with circles representing the GO database, triangles representing the KEGG database, and squares representing the REACTOME database. The color represents the magnitude of the p-value, with redder colors indicating smaller p-values.\u003c/p\u003e","description":"","filename":"Figure3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5195612/v1/bfd4ce1df4170350f33aca31.jpg"},{"id":70287268,"identity":"1193c59b-3a29-432c-bba6-aa81701540cd","added_by":"auto","created_at":"2024-12-01 16:51:06","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1982131,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal changes in gene expression across multiple time points. (\u003cstrong\u003eA\u003c/strong\u003e) Analysis plot for determining the optimal number of gene clusters based on the total within-cluster sum of squares and the \"gap\" statistic. (\u003cstrong\u003eB\u003c/strong\u003e) Heatmap of genes with different expression patterns. The col-ors on the y-axis represent different gene expression pattern clusters, while the colors on the x-axis represent different sample groups. The intensity of the heatmap colors indicates the ex-pression levels, with red representing high expression and blue representing low expression. (\u003cstrong\u003eC\u003c/strong\u003e) Line plots showing the average gene expression trends for different expression patterns. (\u003cstrong\u003eD\u003c/strong\u003e) Line plots of different gene expression patterns. The line colors represent the membership of genes in the clusters, with darker red indicating higher membership and darker blue indicating lower membership in the respective cluster.\u003c/p\u003e","description":"","filename":"Figure4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5195612/v1/650d6c7702bf2ad905d512ee.jpg"},{"id":70287270,"identity":"b7b805b1-f4a4-4d0d-a52e-4d92981c9870","added_by":"auto","created_at":"2024-12-01 16:51:06","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2993603,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis of different gene expression pattern clusters. (\u003cstrong\u003eA\u003c/strong\u003e) Enrichment analysis based on the GO database. (\u003cstrong\u003eB\u003c/strong\u003e) Enrichment analysis based on the KEGG database. The x-axis represents different gene expression pattern clusters, and the y-axis represents pathway entries. The color of the points represents different gene expression pattern clusters. The number of genes in a pathway is represented by the size of the points, with larger points indicating a greater number of genes.\u003c/p\u003e","description":"","filename":"Figure5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5195612/v1/7795ce7e59313aab9edb1e0f.jpg"},{"id":70287264,"identity":"b182dfcf-17a2-4d77-9526-f6a91f7c2303","added_by":"auto","created_at":"2024-12-01 16:51:06","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2224565,"visible":true,"origin":"","legend":"\u003cp\u003eWGCNA identified gene co-expression modules at different time points post PEDV in-fection. (\u003cstrong\u003eA\u003c/strong\u003e) Sample clustering tree at different time points post-infection. (\u003cstrong\u003eB\u003c/strong\u003e) Selection of the soft-thresholding powers (β). The left panel displays the scale-free fit index in relation to the soft-thresholding power. The right panel shows the mean connectivity versus the soft-thresholding power. The fit index curve for power 14 was chosen because it flattens out at a high value (\u0026gt;0.8). (\u003cstrong\u003eC\u003c/strong\u003e) Hierarchical cluster dendrogram of samples at different time points post-infection, showing co-expression modules generated using WGCNA. Modules belonging to branches are color-coded according to the interconnectedness of genes. Eight modules repre-sented by colors in the horizontal bar were found using a 0.25 threshold for merging. (\u003cstrong\u003eD\u003c/strong\u003e) Heatmap showing gene expression of the 8 modules across four time points. (\u003cstrong\u003eE\u003c/strong\u003e) Relationship between modules and time points post PEDV infection. The correlation heatmap displays the correlations between modules, with the color intensity representing the strength of the correla-tion. Deeper red indicates a stronger positive correlation between modules, while deeper blue indicates a stronger negative correlation. The lines connecting the time points and the 8 modules represent the associations between the traits and the co-expression modules. The line thickness represents the strength of the association, with thicker lines indicating stronger associations. The line colors represent the p-values of the associations, with deep red for \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01, orange for 0.01 \u0026lt; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, and gray for \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05. (\u003cstrong\u003eF\u003c/strong\u003e) Scatter plot of gene significance vs. module membership for modules associated with time points post-infection. The y-axis represents gene significance, which indicates the degree of association between a gene and the infection time point trait. Genes with high significance may have important biological functions or regulatory roles under specific phenotypic conditions. The x-axis represents module membership, with higher values indicating that a gene's expression pattern is more closely correlated with the overall module. The color of the points represents the four modules associated with the time points post-infection: deep red, cyan, brown, and dark gray modules.\u003c/p\u003e","description":"","filename":"Figure6.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5195612/v1/67625920bb65fd9572ff4f1a.jpg"},{"id":70287265,"identity":"64e48ca0-eba9-4c4a-9258-c2f5c5ce6e3c","added_by":"auto","created_at":"2024-12-01 16:51:06","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2169068,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis of genes within co-expression modules associated with PEDV in-fection time. (\u003cstrong\u003eA\u003c/strong\u003e) Bubble plot of GO enrichment analysis. (\u003cstrong\u003eB\u003c/strong\u003e) Bubble plot of KEGG enrichment analysis. (\u003cstrong\u003eC\u003c/strong\u003e) Bubble plot of Reactome enrichment analysis. The x-axis represents the enrichment factor, which is the ratio of the number of enriched genes in a pathway to the number of back-ground genes. The size of the dots represents the number of genes, with larger dots indicating a greater number of genes. The color of the dots represents the magnitude of the p-value.\u003c/p\u003e","description":"","filename":"Figure7.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5195612/v1/c7e1b1cbea57922cd32a4f04.jpg"},{"id":70287271,"identity":"22c478a3-adf1-4a69-a54a-c799e2ddae8c","added_by":"auto","created_at":"2024-12-01 16:51:06","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2359191,"visible":true,"origin":"","legend":"\u003cp\u003eProtein interaction network revealing core genes and GSEA enrichment analysis. (\u003cstrong\u003eA\u003c/strong\u003e) Protein interaction network. The size of the nodes represents the importance of the genes in the protein interaction network, with larger nodes indicating more interactions between the protein and other proteins. The top 20 core genes are annotated. (\u003cstrong\u003eB\u003c/strong\u003e) Heatmap of the top twenty core genes. The colors of the horizontal axis represent different sample groups. The depth of color in the heatmap represents the expression level, with red indicating high gene expression and blue indicating low gene expression. (\u003cstrong\u003eC-D\u003c/strong\u003e) GSEA enrichment analysis plot. The x-axis represents the ranking of gene sets, with the ranking decreasing from left to right, while the y-axis represents the enrichment score. The enrichment score curve shows the degree of enrichment of a gene set during the PEDV infection process, with a larger absolute value of the curve peak indicating a higher degree of enrichment of the gene set during the PEDV infection process. (\u003cstrong\u003eE\u003c/strong\u003e) Bar plot of fold change in core gene RNAseq and qPCR expression. y-axis is log2FoldChange and x-axis is gene.\u003c/p\u003e","description":"","filename":"Figure8.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5195612/v1/b856ff287afd46bc9b08157d.jpg"},{"id":72641658,"identity":"01aba3de-ba52-424d-8e42-7a019c5548a2","added_by":"auto","created_at":"2024-12-30 16:11:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":22127828,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5195612/v1/4d288f45-e61b-4f2e-bc90-5c232937e2dd.pdf"},{"id":70287815,"identity":"83b30116-95a6-4500-a526-cb3bf59c2a87","added_by":"auto","created_at":"2024-12-01 16:59:06","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3793003,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.zip","url":"https://assets-eu.researchsquare.com/files/rs-5195612/v1/c9154a7c95ba78253806daf0.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptional Analysis Reveals Suppression of RAD51 and Disruption of the Homologous Recombination Pathway during PEDV Infection of IPEC-J2 Cells","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePorcine epidemic diarrhea virus (PEDV) is a highly contagious enteric pathogen that induces severe diarrhea, dehydration, and high mortality rates in neonatal piglets [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. PEDV, a member of the family Coronaviridae [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], is a single-stranded positive-sense RNA virus with a genome size of approximately 28 kb, comprising seven open reading frames that code for the ORF1a, ORF1b, S, ORF3, E, M, and N proteins [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Since the identification of the prototype strain in 1978 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], PEDV infections have been reported annually across the globe, resulting in significant economic losses for the swine industry [\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Despite extensive research, the complete pathological mechanisms of PEDV and the molecular mechanisms of host responses remain elusive.\u003c/p\u003e \u003cp\u003eThe genome's integrity is critical for cell survival and function, and DNA double-strand breaks (DSBs) pose one of the most serious threats to it [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Various factors, including endogenous damage like replication errors and exogenous factors like ionizing radiation and chemicals, can cause DSBs. To cope with these damages, cells have evolved a complex DNA repair mechanism, in which homologous recombination (HR) is one of the major pathways. HR relies on a highly conserved protein network that acts together to restore damaged DNA [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. RAD51 is a core protein in HR, which is a homolog of bacterial RecA and plays a crucial role in eukaryotes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. By forming filamentous nucleoprotein structures, RAD51, an ATP-dependent DNA-binding protein, mediates homologous pairing and strand exchange reactions, enabling the repair of broken DNA fragments using their undamaged homologs as templates [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. \u003cem\u003eRAD51\u003c/em\u003e's function is critical for maintaining genomic stability, and its defects can lead to chromosomal instability and increased cancer incidence [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Apart from its role in DNA repair, \u003cem\u003eRAD51\u003c/em\u003e is also associated with virus infection. Increasing evidence suggests that \u003cem\u003eRAD51\u003c/em\u003e is involved in the viral replication cycle and may influence the pathogenicity of viruses. For example, studies have shown that \u003cem\u003eRAD51\u003c/em\u003e can interact with HIV-1 integrase, inhibiting its activity and limiting HIV-1 replication [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. \u003cem\u003eRAD51\u003c/em\u003e also plays a part in HBV infection by protecting the genome and helping to fix homologous DNA to support HBV replication [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we used comprehensive bioinformatics analysis processes, including differential expression analysis, time sequence clustering, weighted gene coexpression network analysis (WGCNA), and protein\u0026ndash;protein interactions (PPI) network analysis, to investigate the transcription cluster time scenes of IPEC-J2 cells infected with PEDV at multiple time points (12 h, 24 h, and 48 h after infection) and identify the regulatory pathways and genes that play a critical role in hosts' replication and transmission of the virus. The findings indicate a significant inhibition of the homogenic reorganization pathway during the post-PEDV infection period, with \u003cem\u003eRAD51\u003c/em\u003e, a critical gene, playing a crucial role in this pathway. This suggests that PDEV could potentially disrupt the source reorganization pathway by suppressing \u003cem\u003eRAD51\u003c/em\u003e expression, thereby promoting virus reintegration and transmission. This study provides new insights into understanding the molecular mechanisms of PEDV infection and provides potential targets for developing treatment strategies for PEDV infection. In the future, we can delve deeper into the specific mechanisms of \u003cem\u003eRAD51\u003c/em\u003e's role in PEDV infection and investigate whether pharmacological interventions targeting \u003cem\u003eRAD51\u003c/em\u003e can effectively inhibit the replication and spread of PEPV.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCells culture and virus infection\u003c/h2\u003e \u003cp\u003eIPEC-J2 cells were maintained in DMEM medium (Gibco, USA) with 10% fetal bovine serum (FBS, Gibco, USA) at 37\u0026deg;C and 5% CO\u003csub\u003e2\u003c/sub\u003e. This study used the PEDV CV777 strain, which was generously provided by China Agricultural University. Cells were infected with PEDV at a multiplicity of infection (MOI) of 1 and then cultured in DMEM medium containing 2 \u0026micro;g/mL trypsin at 37\u0026deg;C and 5% CO\u003csub\u003e2\u003c/sub\u003e for 1 h. After incubation, the cells were washed with phosphate-buffered saline and then cultured in DMEM medium containing 2% FBS.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDetection of PEDV-M gene copy number\u003c/h3\u003e\n\u003cp\u003eDesign fluorescent quantitative primers for the M gene based on the PEDV genome information, and detect the cycle number of the PEDV M gene through fluorescent quantitative analysis. Use the standard curve equation established in the early stage for PEDV-CV777 type: y = -3.3354lg(x)\u0026thinsp;+\u0026thinsp;37.832, R2\u0026thinsp;=\u0026thinsp;0.9994, to calculate the copy number of PEDV.\u003c/p\u003e\n\u003ch3\u003eRNA extraction\u003c/h3\u003e\n\u003cp\u003eSamples collected at 12, 24, and 48 h post PEDV infection were subjected to RNA sequencing. IPEC-J2 cells were divided into four groups: cells infected with PEDV for 12 h, cells infected with PEDV for 24 h, cells infected with PEDV for 48 h, and control cells simulated for infection. Each group of cells had four biological replicates. According to the manufacturer's instructions, total RNA was extracted from PEDV-infected and uninfected cells using TRIzol\u0026reg; reagent (Invitrogen, USA). RNA quality and concentration were tested using NanoDrop 2000 (Thermo Scientific, USA) and Agilent 2100 Bioanalyzer (Agilent Technologies, USA). Samples with RNA Integrity Number (RIN)\u0026thinsp;\u0026ge;\u0026thinsp;7 were selected for further analysis.\u003c/p\u003e\n\u003ch3\u003eLibrary construction and sequencing\u003c/h3\u003e\n\u003cp\u003eEach sample used 1 \u0026micro;g of RNA as the input for RNA sequencing library preparation. Following the manufacturer's instructions, the Epicenter Ribo-Zero rRNA Removal Reagent was used to deplete ribosomal RNA, and the NEBNext\u0026reg; Ultra\u0026trade; II Directional RNA Library Prep Kit for Illumina\u0026reg; was used to generate mRNA sequencing libraries. The purity of the library products was then evaluated using the Agilent 2100 Bioanalyzer, and sequencing was performed on the Illumina HiSeq X Ten platform by OE Biotech Co.\u003c/p\u003e\n\u003ch3\u003eAnalysis of differentially expressed mRNAs\u003c/h3\u003e\n\u003cp\u003eDifferential expression gene analysis was performed between the groups (12 h vs Mock, 24 h vs Mock, 48 h vs Mock, 24 h vs 12 h, 48 h vs 12 h, 48 h vs 24 h) using the R package DESeq2 (version: 1.34.0) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and genes with p-adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2FC| \u0026gt;1 were selected as differential genes.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment analysis\u003c/h2\u003e \u003cp\u003eFunctional enrichment analysis of the genes was performed based on the Gene Ontology (GO) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], Kyoto Encyclopedia of Genes and Genomes (KEGG) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and Reactome [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] databases. The hypergeometric distribution test was used with the enrichGO and enrichKEGG functions from the clusterProfiler R package (version 4.2.2) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] to identify enriched pathways from the GO and KEGG databases. Additionally, the enrichPathway function from the ReactomePA package was used to perform enrichment analysis on the Reactome database. Pathways with p-values less than 0.05 were retained for further analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTime-series Analysis\u003c/h3\u003e\n\u003cp\u003eSoft clustering analysis was performed using the fuzzy c-means algorithm provided by the Mfuzz R package (version 2.54.0) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] to identify different expression patterns of genes in the time-series experimental design. Two parameters, c (number of clusters) and m (fuzziness parameter), were required for this analysis. The value of parameter c was determined by evaluating the change in the sum of squared errors as the number of clusters increased, and the value of parameter m was obtained using the mestimate function from the Mfuzz package. After determining the two key parameters, clustering was performed, and genes with a membership degree\u0026thinsp;\u0026gt;\u0026thinsp;0.6 were retained to ensure similar expression trends within each group.\u003c/p\u003e\n\u003ch3\u003eConstruction of Weighted Gene Co-expression Network\u003c/h3\u003e\n\u003cp\u003eWeighted gene co-expression network analysis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] was used to construct gene co-expression modules from the gene expression profiles. First, a gene relationship matrix was obtained from the gene expression profiles using Pearson correlation coefficients. By setting the soft threshold β to 14, the gene relationship matrix derived from Pearson correlation coefficients was transformed into an adjacency matrix. Then, the topological overlap matrix (TOM) was calculated to measure the interconnectivity of the network. We used the dissimilarity of TOM as the clustering distance to divide the genes into different modules. Additionally, a dynamic tree-cutting algorithm was applied, with a threshold of 0.25, to merge similar gene modules.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eProtein-Protein Interaction Network Analysis\u003c/h2\u003e \u003cp\u003eProtein-protein interaction information for the corresponding genes was retrieved from the STRING database (version 12.0) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A minimum interaction confidence score threshold of 0.4 was set, retaining only interactions with a confidence score greater than or equal to 0.4. The protein-protein interaction network was constructed using Cytoscape software. Topological analysis of the network was performed, calculating node-specific metrics such as degree and betweenness centrality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative real time‑PCR (qPCR) verification\u003c/h2\u003e \u003cp\u003eBased on the gene sequences published in the GenBank database, qPCR primers were designed using Primer Premier 5.0 software, with GAPDH as the reference gene. All primers were synthesized by Sangon Biotech (Shanghai) Co., Ltd. Real-time quantitative PCR (qPCR) analysis was performed using a real-time fluorescence detection kit. All qPCR reactions were carried out in a 20 \u0026micro;L volume, with 10 \u0026micro;L of 2\u0026times; SYBR Premix ExTapTM II, 0.4 \u0026micro;L of 10 \u0026micro;mol/L PCR Forward Primer, 0.4 \u0026micro;L of 10 \u0026micro;mol/L PCR Reverse Primer, 0.4 \u0026micro;L of 50\u0026times; ROX Reference Dye II, 2.0 \u0026micro;L of cDNA, and RNase-free dH2O to a total volume of 20 \u0026micro;L. Three independent experimental replicates were set up for each sample. The qPCR amplification program was as follows: 95\u0026deg;C for 5 min; 95\u0026deg;C for 10 s, 60\u0026deg;C for 30 s, for a total of 40 cycles. To analyze the specificity of the amplification products, multiple data points were collected after the PCR amplification, and melting curve analysis was performed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis and Data Visualization\u003c/h2\u003e \u003cp\u003eAll statistical analysis is done in R environment and the visualization of the data is done using the R package ggplot2.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDifferential transcriptomic landscapes delineate PEDV infection dynamics at multiple time points\u003c/h2\u003e \u003cp\u003eNormal IPEC-J2 cells exhibited irregular shapes with clear outlines and distinct boundaries, evenly distributed on the cell plate. After infection with the classical PEDV strain CV777, the cells showed obvious shrinkage, became rounded, and lost their normal cellular morphology, displaying typical cytopathic effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). qPCR results revealed that the copy number of the PEDV-M gene gradually increased at 12 h and 24 h post-infection, reaching the highest expression level at 24 h, and slightly decreased at 48h (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequently, transcriptome sequencing was performed on samples from different infection time points. The boxplot results demonstrated relatively uniform distribution of TPM values for all sample genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). PCA results indicated that the genes within each group exhibited similar expression patterns, while the samples between groups could be well distinguished (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). These results suggested that the sequencing data quality was satisfactory for further bioinformatics analysis. Differential analysis revealed 2,275 (611 upregulated and 1,664 downregulated), 1,492 (609 upregulated and 883 downregulated), 3,409 (2,093 upregulated and 1,316 downregulated), 2,231 (1,509 upregulated and 722 downregulated), 5,417 (3,398 upregulated and 2,019 downregulated), and 2,703 (1,951 upregulated and 752 downregulated) DEGs in the 12 h vs Mock, 24 h vs Mock, 48 h vs Mock, 24 h vs 1 2h, 48 h vs 12 h, and 48 h vs 24 h comparison groups, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEnrichment analysis showed that pathways such as the JAK-STAT signaling pathway, MAPK signaling pathway, cytokine-cytokine receptor interaction, and PI3K-Akt signaling pathway were enriched in the DEGs of the Mock group. Immune-related pathways, including regulation of T cell apoptotic process, regulation of lymphocyte differentiation, and regulation of adaptive immune response, were only enriched in the DEGs of the 48 h vs Mock group. Pathways such as metabolism of carbohydrates, HIF-1 signaling pathway, and positive regulation of lipid transport were exclusively enriched in the 12 h vs Mock group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eTime-course transcriptomics uncvers transcriptional reprogramming in PEDV infection\u003c/h2\u003e \u003cp\u003eTo investigate the gene expression trends across different groups, this study employed a soft-threshold clustering method based on the within-group sum of squares \"gap\" statistic (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) and classified the gene expression patterns into 5 clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The gene expression heatmap displayed the expression patterns of genes from Cluster 1 to Cluster 5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Genes in Cluster 1 exhibited an upward trend in expression after PEDV infection, and enrichment analysis revealed that these genes were mainly involved in antiviral pathways such as defense response to virus, regulation of immune system process, and TNF signaling pathway. Conversely, genes in Cluster 3 and Cluster 4 showed a downward trend in expression following PEDV infection and were primarily enriched in metabolism-related pathways, including cellular amide metabolic process, iposaccharide metabolic process, and carbohydrate derivative metabolic process. Furthermore, Cluster 2 and Cluster 5 displayed irregular expression patterns over time and were mainly enriched in pathways such as RNA splicing, rRNA processing, cell cycle, and tight junction (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B and Table S2, 3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eWGCNA identifies time-specific co-expression modules in PEDV infection\u003c/h2\u003e \u003cp\u003eThe study utilized WGCNA analysis to investigate gene co-expression networks associated with the PEDV infection process. The sample clustering tree revealed no outlier samples, indicating that all samples could be further analyzed using WGCNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). By setting the soft threshold β to 14 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), WGCNA successfully divided the genes into eight co-expression modules represented by different colors (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), with each module exhibiting distinct expression patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Among these modules, the brown gene module (r=-0.64, p\u0026thinsp;=\u0026thinsp;8e-03), cyan gene module (r\u0026thinsp;=\u0026thinsp;0.76, p\u0026thinsp;=\u0026thinsp;7e-04), darkred gene module (r=-0.85, p\u0026thinsp;=\u0026thinsp;3e-05), and darkgrey gene module (r\u0026thinsp;=\u0026thinsp;0.72, p\u0026thinsp;=\u0026thinsp;2e-03) showed significant correlations with the PEDV infection process (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eTo further explore the functions of the gene modules significantly associated with the PEDV infection process, we performed enrichment analysis on the key genes (MM\u0026thinsp;\u0026ge;\u0026thinsp;0.8, GS\u0026thinsp;\u0026ge;\u0026thinsp;0.8) within the brown, cyan, darkred, and darkgrey gene modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). GO enrichment analysis revealed that these genes were primarily involved in biological pathways such as T cell proliferation, positive regulation of cell-cell adhesion, and regulation of cell population proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). KEGG enrichment analysis showed enrichment in pathways including TNF signaling pathway, viral protein interaction with cytokine and cytokine receptor, Toll-like receptor signaling pathway, and JAK-STAT signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Reactome enrichment analysis indicated enrichment in biological pathways such as extracellular matrix organization, homology-directed repair, and KK complex recruitment mediated by RIP1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC).\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePPI network analysis uncovers regulatory hubs in PEDV infection\u003c/h2\u003e \u003cp\u003eTo identify key core regulatory factors during the PEDV infection process, this study conducted protein interaction network analysis and found that among the 985 key genes associated with PEDV infection, the top 20 core genes in the interaction network included 6 upregulated genes (\u003cem\u003eTFRC\u003c/em\u003e, \u003cem\u003eSUOX\u003c/em\u003e, \u003cem\u003eRMI1\u003c/em\u003e, \u003cem\u003eCD74\u003c/em\u003e, \u003cem\u003eIFIH1\u003c/em\u003e, and \u003cem\u003eCD86\u003c/em\u003e) and 14 downregulated genes (\u003cem\u003eFOS\u003c/em\u003e, \u003cem\u003eCDC6\u003c/em\u003e, \u003cem\u003eCDCA3\u003c/em\u003e, \u003cem\u003ePIK3R2\u003c/em\u003e, \u003cem\u003eTUFM\u003c/em\u003e, \u003cem\u003eVARS\u003c/em\u003e, \u003cem\u003eASF1B\u003c/em\u003e, \u003cem\u003ePOLD1\u003c/em\u003e, \u003cem\u003eMCM8\u003c/em\u003e, \u003cem\u003ePOLA1\u003c/em\u003e, \u003cem\u003eCDC45\u003c/em\u003e, \u003cem\u003eBCS1L\u003c/em\u003e, \u003cem\u003eRAD51\u003c/em\u003e, and \u003cem\u003eRPA2\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, B). GSEA enrichment analysis of the 48 h vs Mock group revealed that among the 20 core genes, only \u003cem\u003eRAD51\u003c/em\u003e, \u003cem\u003eCDC6\u003c/em\u003e, and \u003cem\u003eRPA2\u003c/em\u003e were enriched, primarily participating in pathways such as DNA replication and homologous recombination (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC, D).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePEDV has resulted in substantial economic losses within the livestock industry, and existing commercial vaccines have not been able to offer complete protection for pigs. Despite advancements in PEDV research over recent decades, a comprehensive understanding of the virus's pathogenic mechanisms remains elusive. This investigation utilized bioinformatics analysis to examine the transcriptional alterations in PEDV-infected IPEC-J2 cells across various time intervals, identifying DEGs, time-specific co-expression modules, and crucial regulatory elements linked to PEDV infection. Through WGCNA analysis, gene co-expression modules significantly associated with the progression of PEDV infection were identified. These modules play pivotal roles in the immune response and metabolic regulation of host cells during PEDV infection. The findings of this study indicate that host cell immune responses are triggered following PEDV infection, resulting in the proliferation of immune cells and the modulation of immune-related pathways. The innate immune system acts as the primary defense against viral infections, with pattern recognition receptors (PRRs) identifying viral-associated molecular patterns, initiating downstream signaling pathways, and generating antiviral substances like interferon [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, viruses have developed various strategies to evade innate immunity, including inhibiting PRR recognition, disrupting interferon signaling pathways, and suppressing the expression of antiviral genes [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Additionally, PEDV may manipulate the metabolic processes of host cells to create a conducive environment for its replication and survival. The interaction between PEDV and host cells also influences cellular functions such as RNA splicing [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], cell cycle regulation [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and cell-cell connections [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne significant discovery is the notable inhibition of the homologous recombination pathway following PEDV infection. HR is a DNA repair mechanism essential for maintaining genome stability and integrity [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. RAD51, a core protein of HR responsible for homologous pairing and strand exchange reactions to ensure accurate repair of DSBs [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], is downregulated in PEDV-infected IPEC-J2 cells. This indicates that PEDV may disrupt the HR pathway, potentially facilitating viral replication and spread by suppressing \u003cem\u003eRAD51\u003c/em\u003e expression. Decreased \u003cem\u003eRAD51\u003c/em\u003e levels have been linked to increased chromosomal instability and cancer risk. In viral infections, \u003cem\u003eRAD51\u003c/em\u003e is associated with the replication cycle and pathogenicity of various viruses [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. For instance, \u003cem\u003eRAD51\u003c/em\u003e interacts with the replication initiator protein of Begomovirus to enhance viral replication [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In astrocytes, elevated \u003cem\u003eRAD51\u003c/em\u003e levels boost HIV-1 LTR activity in conjunction with HIV-1 Tat, transcription factor C/EBPβ, and CHOP [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Knocking down \u003cem\u003eRAD51\u003c/em\u003e significantly reduces HCV protein expression and intracellular and extracellular HCV RNA levels pre-HCV infection [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Through protein-protein interaction network analysis, this study identified key regulatory factors of PEDV infection. The top 20 genes in the network, including \u003cem\u003eRAD51\u003c/em\u003e, \u003cem\u003eCDC6\u003c/em\u003e, and \u003cem\u003eRPA2\u003c/em\u003e, are involved in DNA replication, homologous recombination, and other pathways. This underscores the potential roles of these genes in the viral replication cycle and suggests they could be targeted to inhibit PEDV replication and dissemination.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our study provides insights into the molecular mechanisms of PEDV infection and identifies potential targets for developing therapeutic strategies against PEDV infection. PEDV inhibits the homologous recombination pathway by downregulating the expression of \u003cem\u003eRAD51\u003c/em\u003e, which may promote viral replication and dissemination. Furthermore, the dynamic changes in gene expression patterns, involvement of immune-related pathways, and regulation of host cellular processes highlight the complex interaction between PEDV and host cells.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePEDV \u0026nbsp; \u0026nbsp; \u0026nbsp; Porcine epidemic diarrhea virus\u003c/p\u003e\n\u003cp\u003eDEGs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Differentially expressed genes\u003c/p\u003e\n\u003cp\u003eDSBs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;DNA double-strand breaks\u003c/p\u003e\n\u003cp\u003eHR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Homologous recombination\u003c/p\u003e\n\u003cp\u003eWGCNA \u0026nbsp; \u0026nbsp;Weighted gene coexpression network analysis\u003c/p\u003e\n\u003cp\u003ePPI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Protein\u0026ndash;protein interactions\u003c/p\u003e\n\u003cp\u003eIPEC-J2 \u0026nbsp; \u0026nbsp; Porcine small intestinal epithelial cells\u003c/p\u003e\n\u003cp\u003eTOM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;The topological overlap matrix\u003c/p\u003e\n\u003cp\u003ePRRs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Pattern recognition receptors\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.J. conceived and designed the experiments. L.S. and C.F.C. performed the experiments. L.S. and J.B.Y. analyzed the data. J.J. and L.S. wrote the paper. All authors read and approved the final version of the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by Youth Support Project of Jiangsu Vocational College of Agriculture and Forestry (No. 2021kj18); Basic Science (Natural Science) Research Project of Colleges in Jiangsu Province (No. 21KJB230003).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data needed to evaluate the conclusions in the paper are available in the Genome Sequence Archive (GSA) (CRA013464) maintained by the Beijing Institute of Genomics (BIG) Data Center. The names of the repository/repositories and accession number(s) can be found below:\u0026nbsp;https://bigd.big.ac.cn/gsa, CRA019128.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJung K, Annamalai T, Lu Z, Saif LJ. Comparative pathogenesis of US porcine epidemic diarrhea virus (PEDV) strain PC21A in conventional 9-day-old nursing piglets vs. 26-day-old weaned pigs. Vet Microbiol. 2015;178:31\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.vetmic.2015.04.022\u003c/span\u003e\u003cspan address=\"10.1016/j.vetmic.2015.04.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCavanagh D. Nidovirales: a new order comprising Coronaviridae and Arteriviridae. Arch Virol. 1997;142:629\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePensaert MB, de Bouck P. A new coronavirus-like particle associated with diarrhea in swine. Arch Virol. 1978;58:243\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/bf01317606\u003c/span\u003e\u003cspan address=\"10.1007/bf01317606\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheun-Arom T, Temeeyasen G, Tripipat T, Kaewprommal P, Piriyapongsa J, Sukrong S, Chongcharoen W, Tantituvanont A, Nilubol D. Full-length genome analysis of two genetically distinct variants of porcine epidemic diarrhea virus in Thailand. Infect Genet Evol. 2016;44:114\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.meegid.2016.06.046\u003c/span\u003e\u003cspan address=\"10.1016/j.meegid.2016.06.046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavies PR. The dilemma of rare events: Porcine epidemic diarrhea virus in North America. Prev Vet Med. 2015;122:235\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.prevetmed.2015.08.006\u003c/span\u003e\u003cspan address=\"10.1016/j.prevetmed.2015.08.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJung K, Saif LJ. Porcine epidemic diarrhea virus infection: Etiology, epidemiology, pathogenesis and immunoprophylaxis. Vet J. 2015;204:134\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.tvjl.2015.02.017\u003c/span\u003e\u003cspan address=\"10.1016/j.tvjl.2015.02.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang D, Fang L, Xiao S. Porcine epidemic diarrhea in China. Virus Res. 2016;226:7\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.virusres.2016.05.026\u003c/span\u003e\u003cspan address=\"10.1016/j.virusres.2016.05.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Q, Vlasova AN, Kenney SP, Saif LJ. Emerging and re-emerging coronaviruses in pigs. Curr Opin Virol. 2019;34:39\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.coviro.2018.12.001\u003c/span\u003e\u003cspan address=\"10.1016/j.coviro.2018.12.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWood EN. An apparently new syndrome of porcine epidemic diarrhoea. Vet Rec. 1977;100:243\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/vr.100.12.243\u003c/span\u003e\u003cspan address=\"10.1136/vr.100.12.243\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H, Zimmerman SE, Weyemi U. Genomic instability and metabolism in cancer. Int Rev Cell Mol Biol. 2021;364:241\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/bs.ircmb.2021.05.004\u003c/span\u003e\u003cspan address=\"10.1016/bs.ircmb.2021.05.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoynahan ME, Jasin M. Mitotic homologous recombination maintains genomic stability and suppresses tumorigenesis. Nat Rev Mol Cell Biol. 2010;11:196\u0026ndash;207. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrm2851\u003c/span\u003e\u003cspan address=\"10.1038/nrm2851\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenson FE, Stasiak A, West SC. Purification and characterization of the human Rad51 protein, an analogue of E. coli RecA. Embo j. 1994;13:5764\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/j.1460-2075.1994.tb06914.x\u003c/span\u003e\u003cspan address=\"10.1002/j.1460-2075.1994.tb06914.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVisp\u0026eacute; S, Defais M. Mammalian Rad51 protein: a RecA homologue with pleiotropic functions. Biochimie. 1997;79:587\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0300-9084(97)82007-x\u003c/span\u003e\u003cspan address=\"10.1016/s0300-9084(97)82007-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDietlein F, Thelen L, Reinhardt HC. Cancer-specific defects in DNA repair pathways as targets for personalized therapeutic approaches. Trends Genet. 2014;30:326\u0026ndash;39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.tig.2014.06.003\u003c/span\u003e\u003cspan address=\"10.1016/j.tig.2014.06.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCosnefroy O, Tocco A, Lesbats P, Thierry S, Calmels C, Wiktorowicz T, Reigadas S, Kwon Y, De Cian A, Desfarges S, et al. Stimulation of the human RAD51 nucleofilament restricts HIV-1 integration in vitro and in infected cells. J Virol. 2012;86:513\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/jvi.05425-11\u003c/span\u003e\u003cspan address=\"10.1128/jvi.05425-11\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThierry S, Benleulmi MS, Sinzelle L, Thierry E, Calmels C, Chaignepain S, Waffo-Teguo P, Merillon JM, Budke B, Pasquet JM, et al. Dual and Opposite Effects of hRAD51 Chemical Modulation on HIV-1 Integration. Chem Biol. 2015;22:712\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chembiol.2015.04.020\u003c/span\u003e\u003cspan address=\"10.1016/j.chembiol.2015.04.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePasaje CF, Kim JH, Park BL, Cheong HS, Bae JS, Park TJ, Lee JS, Kim Y, Lee HS, Koh I, et al. Lack of association of RAD51 genetic variations with hepatitis B virus clearance and occurrence of hepatocellular carcinoma in a Korean population. J Med Virol. 2011;83:1892\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jmv.22122\u003c/span\u003e\u003cspan address=\"10.1002/jmv.22122\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLove MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13059-014-0550-8\u003c/span\u003e\u003cspan address=\"10.1186/s13059-014-0550-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAleksander SA, Balhoff J, Carbon S, Cherry JM, Drabkin HJ, Ebert D, Feuermann M, Gaudet P, Harris NL, Hill DP, et al. The Gene Ontology knowledgebase in 2023. Genetics. 2023;224. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/genetics/iyad031\u003c/span\u003e\u003cspan address=\"10.1093/genetics/iyad031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/75556\u003c/span\u003e\u003cspan address=\"10.1038/75556\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanehisa M, Furumichi M, Sato Y, Kawashima M, Ishiguro-Watanabe M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2023;51:D587\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkac963\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkac963\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/28.1.27\u003c/span\u003e\u003cspan address=\"10.1093/nar/28.1.27\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMilacic M, Beavers D, Conley P, Gong C, Gillespie M, Griss J, Haw R, Jassal B, Matthews L, May B, et al. The Reactome Pathway Knowledgebase 2024. Nucleic Acids Res. 2024;52:D672\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkad1025\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkad1025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innov (Camb). 2021;2:100141. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.xinn.2021.100141\u003c/span\u003e\u003cspan address=\"10.1016/j.xinn.2021.100141\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar L, M EF. Mfuzz: a software package for soft clustering of microarray data. Bioinformation. 2007;2:5\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.6026/97320630002005\u003c/span\u003e\u003cspan address=\"10.6026/97320630002005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLangfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1471-2105-9-559\u003c/span\u003e\u003cspan address=\"10.1186/1471-2105-9-559\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R, Gable AL, Fang T, Doncheva NT, Pyysalo S, et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023;51:D638\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkac1000\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkac1000\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakeuchi O, Akira S. Pattern recognition receptors and inflammation. Cell. 2010;140:805\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cell.2010.01.022\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2010.01.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYatim KM, Lakkis FG. A brief journey through the immune system. Clin J Am Soc Nephrol. 2015;10:1274\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2215/cjn.10031014\u003c/span\u003e\u003cspan address=\"10.2215/cjn.10031014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin F, Zhang H, Li L, Yang Y, Zou X, Chen J, Tang XPEDV. Insights and Advances into Types, Function, Structure, and Receptor Recognition. Viruses. 2022;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/v14081744\u003c/span\u003e\u003cspan address=\"10.3390/v14081744\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao J, Qin C, Liu Y, Rao Y, Feng P. Herpes Simplex Virus and Pattern Recognition Receptors: An Arms Race. Front Immunol. 2020;11:613799. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2020.613799\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2020.613799\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoudreault S, Roy P, Lemay G, Bisaillon M. Viral modulation of cellular RNA alternative splicing: A new key player in virus-host interactions? Wiley Interdiscip Rev RNA. 2019;10:e1543. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/wrna.1543\u003c/span\u003e\u003cspan address=\"10.1002/wrna.1543\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalamango DJ, Harris RS. Demystifying Cell Cycle Arrest by HIV-1 Vif. Trends Microbiol. 2021;29:381\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.tim.2021.01.001\u003c/span\u003e\u003cspan address=\"10.1016/j.tim.2021.01.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVaswani CM, Varkouhi AK, Gupta S, Ektesabi AM, Tsoporis JN, Yousef S, Plant PJ, da Silva AL, Cen Y, Tseng YC, et al. Preventing occludin tight-junction disruption via inhibition of microRNA-193b-5p attenuates viral load and influenza-induced lung injury. Mol Ther. 2023;31:2681\u0026ndash;701. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ymthe.2023.06.011\u003c/span\u003e\u003cspan address=\"10.1016/j.ymthe.2023.06.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCeccaldi R, Liu JC, Amunugama R, Hajdu I, Primack B, Petalcorin MI, O'Connor KW, Konstantinopoulos PA, Elledge SJ, Boulton SJ, et al. Homologous-recombination-deficient tumours are dependent on Polθ-mediated repair. Nature. 2015;518:258\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nature14184\u003c/span\u003e\u003cspan address=\"10.1038/nature14184\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonilla B, Hengel SR, Grundy MK, Bernstein KA. RAD51 Gene Family Structure and Function. Annu Rev Genet. 2020;54:25\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1146/annurev-genet-021920-092410\u003c/span\u003e\u003cspan address=\"10.1146/annurev-genet-021920-092410\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSon K, Nguyen TTT, Choi JW, Pham LV, Luong TTD, Lim YS, Hwang SB. Rad51 Interacts with Non-structural 3 Protein of Hepatitis C Virus and Regulates Viral Production. Front Microbiol. 2017;8:1249. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmicb.2017.01249\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2017.01249\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang KW, Norberg P, Holmudden M, Elias P, Liljeqvist J. Rad51 and Rad52 are involved in homologous recombination of replicating herpes simplex virus DNA. PLoS ONE. 2014;9:e111584. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0111584\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0111584\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuyal G, Mukherjee SK, Choudhury NR. The host factor RAD51 is involved in mungbean yellow mosaic India virus (MYMIV) DNA replication. Arch Virol. 2013;158:1931\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00705-013-1675-x\u003c/span\u003e\u003cspan address=\"10.1007/s00705-013-1675-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChipitsyna G, Sawaya BE, Khalili K, Amini S. Cooperativity between Rad51 and C/EBP family transcription factors modulates basal and Tat-induced activation of the HIV-1 LTR in astrocytes. J Cell Physiol. 2006;207:605\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jcp.20612\u003c/span\u003e\u003cspan address=\"10.1002/jcp.20612\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"virology-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"virj","sideBox":"Learn more about [Virology Journal](http://virologyj.biomedcentral.com/)","snPcode":"12985","submissionUrl":"https://submission.nature.com/new-submission/12985/3","title":"Virology Journal","twitterHandle":"@VirologyJ","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"PEDV, IPEC-J2, DEGs, Transcriptome, Molecular mechanism","lastPublishedDoi":"10.21203/rs.3.rs-5195612/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5195612/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePEDV is a highly contagious enteric pathogen that can lead to severe diarrhea and death in neonatal pigs. Despite extensive research, the complete pathomechanism of PEDV and the molecular mechanisms of host responses remain unclear. In this study, differentially expressed genes (DEGs), time-specific co-expression modules, and key regulatory genes associated with PEDV infection were identified. Differential analysis showed that 2,275, 1,492, and 3,409 differential genes were screened in the 12 h vs. Mock, 24 h vs. Mock, and 48 h vs. Mock, respectively. Time series analysis showed that the genes of the up-regulated module were mainly involved in antiviral pathways such as viral defense response and regulation of immune system processes. Protein interaction network analysis revealed that the top 20 core genes in the interaction network included six up-regulated genes (\u003cem\u003eTFRC\u003c/em\u003e, \u003cem\u003eSUOX\u003c/em\u003e, \u003cem\u003eRMI1\u003c/em\u003e, \u003cem\u003eCD74\u003c/em\u003e, \u003cem\u003eIFIH1\u003c/em\u003e, \u003cem\u003eCD86\u003c/em\u003e) and 14 down-regulated genes (\u003cem\u003eFOS\u003c/em\u003e, \u003cem\u003eCDC6\u003c/em\u003e, \u003cem\u003eCDCA3\u003c/em\u003e, \u003cem\u003ePIK3R2\u003c/em\u003e, \u003cem\u003eTUFM\u003c/em\u003e, \u003cem\u003eVARS\u003c/em\u003e, \u003cem\u003eASF1B\u003c/em\u003e, \u003cem\u003ePOLD1\u003c/em\u003e, \u003cem\u003eMCM8\u003c/em\u003e, \u003cem\u003ePOLA1\u003c/em\u003e, \u003cem\u003eCDC45\u003c/em\u003e, \u003cem\u003eBCS1L\u003c/em\u003e, \u003cem\u003eRAD51\u003c/em\u003e, \u003cem\u003eRPA2\u003c/em\u003e). In addition, GSEA enrichment analysis showed that pathways such as DNA replication and homologous recombination were significantly inhibited during viral infection, and \u003cem\u003eRAD51\u003c/em\u003e, \u003cem\u003eCDC6\u003c/em\u003e, and \u003cem\u003eRPA2\u003c/em\u003e were involved. Our findings not only reveal dynamic changes in the transcriptome profile of PEDV-infected IPEC-J2 cells, but also provide novel insights into the mechanism of PEDV infection of the host.\u003c/p\u003e","manuscriptTitle":"Transcriptional Analysis Reveals Suppression of RAD51 and Disruption of the Homologous Recombination Pathway during PEDV Infection of IPEC-J2 Cells","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-01 16:51:01","doi":"10.21203/rs.3.rs-5195612/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-12T15:36:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-06T07:43:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-31T04:38:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-19T04:31:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"225828664644487640792067348503375940322","date":"2024-10-14T03:28:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164317049171939104896695250078062358752","date":"2024-10-14T01:45:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147567923195198299154447212582110584207","date":"2024-10-11T02:30:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-08T15:05:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-07T08:34:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-07T08:27:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Virology Journal","date":"2024-10-03T03:25:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"virology-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"virj","sideBox":"Learn more about [Virology Journal](http://virologyj.biomedcentral.com/)","snPcode":"12985","submissionUrl":"https://submission.nature.com/new-submission/12985/3","title":"Virology Journal","twitterHandle":"@VirologyJ","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"51ce1740-d004-4734-9340-274f9537cec3","owner":[],"postedDate":"December 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-30T16:10:31+00:00","versionOfRecord":{"articleIdentity":"rs-5195612","link":"https://doi.org/10.1186/s12985-024-02611-8","journal":{"identity":"virology-journal","isVorOnly":false,"title":"Virology Journal"},"publishedOn":"2024-12-28 15:57:41","publishedOnDateReadable":"December 28th, 2024"},"versionCreatedAt":"2024-12-01 16:51:01","video":"","vorDoi":"10.1186/s12985-024-02611-8","vorDoiUrl":"https://doi.org/10.1186/s12985-024-02611-8","workflowStages":[]},"version":"v1","identity":"rs-5195612","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5195612","identity":"rs-5195612","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}