Transcription Analysis of Chicken Embryo Fibroblast Cells Infected with a Recombinant Avian Leukosis Virus Isolate GX14FF03

Transcription Analysis of Chicken Embryo Fibroblast Cells Infected with a Recombinant Avian Leukosis Virus Isolate GX14FF03 | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Transcription Analysis of Chicken Embryo Fibroblast Cells Infected with a Recombinant Avian Leukosis Virus Isolate GX14FF03 Peikun Wang, Qiuhong Li, Jing Wang, Qiaomu Deng, Min Li, Ping Wei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-888600/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Infection with recombinant avian leukosis virus (ALV) has previously been linked to malignancies and immunosuppression. However, the processes behind recombinant ALV's unique pathophysiology are poorly understood. The study aims to investigate the gene expression patterns of a recombinant ALV isolate GX14FF03 infected chicken fibroblast cells (CEFs) and used the RNA-Sequence technique to undertake a complete analysis of the mRNAs transcriped. As a consequence, a total of 907 significant differentially expressed genes (SDEGs) were identified. Among these SDEGs, the most significantly up-regulated gene was interleukin 8-like 1 (IL8L1), while the most significantly down-regulated gene was fibroblast growth factor 16 (FGF16). The 907 SDGEs were highly enriched (p < 0.05) for 252 Gene Ontology (GO) terms, including 197 BP, 3 CC, and 52 MF. According to the KEGG data analysis, SDEGs are implicated in eight significant pathways (p < 0.05). Furthermore, PPI network analyses revealed that IL8L1 interacts with 17 genes. These findings provide light on the molecular mechanisms of the recombinant ALV infection by explaining the mRNA expression profile in CEFs infected with GX14FF03 virus. Virology Recombinant avian leukosis virus CEF cells RNA-seq Transcription Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Avian leukosis virus (ALV) is a retrovirus that can cause tumorigenesis in chickens when given exogenously [ 1 ]. ALV that infect chickens comprise 7 envelope subgroups designated A–E, J, and K [ 2 ]. Among these subgroups, subgroup J, first isolated in the United Kingdom in 1988, and which later proved to have a broader neoplastic spectrum [ 3 ]. Subgroups A and B are classical and common pathogenic exogenous viruses that induce lymphoid leukosis [ 4 ]. Despite current purification strategies, ALVs remains a serious problem in the poultry industry for there is no effective means of dealing with them [ 5 ]. Transcriptional profiling examination of the host response to an infectious agent, which provides new insights into the interaction between the host and the infectious organism [ 6 ]. Several in vivo and in vitro investigations have revealed the ALV infected birds and cells have differently expressed genes or proteins [ 7 ]. However, there are few studies that focused on the recombinant ALV [ 8 ]. In this study, RNA sequencing was performed to investigate the expression pattern of mRNAs in chicken embryo fibroblast cells infected by recombinant avian leukosis virus isolate GX14FF03. Methods Virus and cells A recombinant avian leukosis virus isolate GX14FF03 (GenBank accession number KU923579) was previously isolated from a commercial native chicken in Guangxi Province [9]. The virus r GX14FF03 used in this study were rescued by using reverse genetics technique and identified [10]. Our previous research showed that this isolate caused body weight decrease and immunosuppression in experimental chickens. Chicken embryo fibroblasts (CEF) were cultured in Dulbecco’s modified eagle medium (DMEM) containing 5% fetal bovine serum (FBS) at 37 ℃ and 5% CO 2 until they reached about 80% confluence. Sample preparation and mRNA sequencing The virus r GX14FF03 was diluted to 50 % tissue culture infective dose (TCID50) of 10 4 and inoculated into the CEFs for 2 h, then the cells were maintained in DMEM with 1% FBS, and harvested at 72 h of post-infection. The r GX14FF03 infected CEFs and control CEFs were processed for mRNA sequencing and analysis by Novoene company, Beijing, China. The Illumina Novaseq 6000 platform was used for mRNA sequencing. Differential expression mRNA analysis Differential expression mRNA analysis was performed using the DESeq2 R package (1.20.0). Genes with an adjusted P-adj|2|found by DESeq2 were assigned as differentially expressed. Gene Ontology (GO) enrichment analysis of differentially expressed genes was implemented by the ClusterProfiler R package, in which gene length bias wascorrected [11]. GO terms with corrected p-value less than 0.05 were considered as significantly enriched. KEGG is a database resource for understanding high-level functions and utilities of the biological system [12]. ClusterProfiler R package were used to test the statistical enrichment of differential expression genes in KEGG pathways. KEGG pathways with corrected p-value less than 0.05 were considered as significantly enriched. Protein-to-protein interaction (PPI) network analyses of SDEGs were performed using STRING database (https://string-db.org/) [13]. PPI network visualization was created with Cytoscape (version 3.8.2), and core proteins were identified by calculating the number of interactions between each network node [14] . Quantitative real-time PCR analysis for DEGs The DEGs identified through transcriptome sequencing were validated using the reverse transcription quantitative real-time PCR (RT-qPCR). For RT-qPCR validation, IL6, IL10, FGF16, SMOC1, TLR15, IL8L1, MYLK, and ACOD1 were used. Primer Express® software for the Quantitative real-time PCR (qPCR) was used to design target-specific primers for the randomly selected genes, as indicated in Table 1. Total RNA samples were extracted and reverse transcribed to cDNA according to the manufacturer’s guidelines of the used RNA extraction kit (TIANGN, China) and PrimeScriptTM kit (Takara, China). The final volume is 20 µl containing 0.4 µl Dye, 0.4 µl of each primer, 2 µl of cDNA, 10 µl SYBR and 6.8 µl water. Assays were performed in triplicate. Analysis was performed by the 2 -∆∆CT method. Table 1 Primers for RT-qPCR in this study Name Primer sequence Name Primer sequence IL6F AAATTCGGTACATCCTCGACGG TLR15F AACCTGGTGCATTTGAGAACCTGC IL6R GGAAGGTTCAGGTTGTTTTCTGC TLR15R TTTCAGGTGAGGTGCAAGACCAGA IL10F TTGCTGGAGGACTTTAAGGGT IL8L1F CCTCACTGCAAGAATGTGGA IL10R CTTGATGTCTGGGTCTTGGTT IL8L1R GGAGGAGGTAGGACGTTTTTG FGF16F GTGCATGGACCGGACTTTCTGTG MYLKF AAGATTGAAGGATACCCAGAC FGF16R TGTCAGCTTCTTCGACCCATAG MYLKR TCCATCGTTTCCACAATGAG SMOC1F GGCTATTAACTCAGCAGCACCTACT ACOD1F TGCTGCTGCGTCCAAGTTT SMOC1R TGTTATTGTCCAACTGGCTGAAGT ACOD1R GGGGCTTAGTCTGAGTGGC Statistical analysis Data were analyzed using GraphPad Prism, version 5.0, software and are expressed as means. The one-way ANOVA analysis was used to assess differences among clusters in the Statistical Package for Social Science Windows, version 19, software. Data deposition The raw sequence data were deposited in the NCBI database Sequence Read Archive with the accession number PRJNA757623. Results Evaluation of the transcriptome sequencing data Total RNA was extracted from GX14FF03 or mock-infected CEFs to examine gene expression. At least 6.06 Gb of clean data from each sample (39.24GB ) was obtained from the transcriptome sequencing. For all samples, the Q20 and Q30 percentages of clean data were higher than 97.50 % and 94.00 %, respectively. For all samples, the GC content of the clean data varied from 50.31 to 51.43 % (Table 2 ). Approximately 87.2-90.98% of clean reads were uniquely mapped to the reference Gallus gallus genome (Hisat2 v2.0.5. ). Through normalisation and statistical analysis, a total of 907 significant differentially expressed genes (SDEGs) were identified, including 534 genes up-regulated and 373 genes down-regulated, 822 genes were known, and 85 genes were novel (Fig. 1 A, Supplementary table S1). At the top 30 SEDGs, 26 were up-regulated and only 4 (FGF16, NKAN4, SMOC1, MYLK ) were down-regulated (Fig. 1 B). Interleukin 8-like 1 (IL8L1) was the most significantly up-regulated gene with a log2FoldChange of 7.618, while fibroblast growth factor 16 (FGF16) was the most significantly down-regulated gene with a log2FoldChange of -4.111 (Supplementary table S1). Table 2 Summary statistics for sequence quality control and mapped data of samples Sample Raw reads Clean reads Q20 (%) Q30 (%) GC pct (%) Total mapped Unique mapped Multi mapped Mock 72h 1 47,504,588 45,787,868 97.50 94.00 51.08 40,732,727 39,928,848 803,879 Mock 72h 2 45,575,230 44,426,782 98.09 94.90 50.31 41,121,076 40,418,091 702,985 Mock 72h 3 41,586,088 40,408,138 98.01 94.57 50.49 37,390,145 36,755,609 634,536 ALV-B 72h 1 42,551,534 41,474,616 97.94 94.61 51.43 37,948,237 37,233,912 714,325 ALV-B 72h 2 46,824,782 45,451,720 98.11 94.99 50.79 41,871,050 41,088,886 782,164 ALV-B 72h 3 45,293,416 44,047,452 98.02 94.72 50.93 40,580,093 39,806,401 773,692 Gene Ontology (Go) Analysis To generally described the functions of genes obtained from RNA-Seq, functional annotations were performed by comparing the sequences with the GO databases. Genes annotated by GO database were classified into the three GO categories, namely, biological progress (BP), cellular component (CC), and molecular function (MF) [ 15 ]. The significance of GO term enrichment in the DEGs was based on a p-value < 0.05. A total of 907 sDGEs were enriched for 252 GO terms were significantly enriched, including 197 BP, 3 CC (cell surface, external side of plasma membrane, receptor complex) and 52 MF (Supplementary table S2). We are concerned here with the top 30 GO terms, which contained 24 BP and 6 MF. (Fig. 2 A, Table 3 ). In biological progress, the primary subcategories are the inflammatory response Table 3 Functional analysis of DEGs using GO and showing the top 30 GO terms Description Category P -value Gene No. Up Gene No. Down Gene No. inflammatory response BP 1.77E-18 52 46 6 receptor regulator activity MF 6.96E-16 49 36 13 receptor ligand activity MF 2.32E-15 46 33 13 chemokine receptor binding MF 1.42E-13 16 16 0 cytokine activity MF 1.58E-13 25 24 1 chemokine activity MF 2.22E-13 14 14 0 cell chemotaxis BP 2.10E-12 33 26 7 positive regulation of immune system process BP 5.19E-12 58 49 9 response to other organism BP 1.08E-11 54 44 10 response to external biotic stimulus BP 1.31E-11 54 44 10 regulation of immune response BP 1.74E-11 50 44 6 response to biotic stimulus BP 3.99E-11 54 44 10 regulation of signaling receptor activity BP 9.03E-11 39 23 16 neutrophil migration BP 1.04E-10 18 17 1 neutrophil chemotaxis BP 1.37E-10 17 16 1 leukocyte chemotaxis BP 1.38E-10 25 21 4 response to bacterium BP 1.57E-10 40 33 7 granulocyte chemotaxis BP 1.96E-10 18 17 1 granulocyte migration BP 2.40E-10 19 18 1 response to molecule of bacterial origin BP 3.81E-10 27 25 2 leukocyte migration BP 4.68E-10 30 25 5 myeloid leukocyte migration BP 1.15E-09 22 19 3 chemotaxis BP 1.31E-09 44 36 8 taxis BP 1.44E-09 44 36 8 activation of immune response BP 2.04E-09 34 32 2 cell activation BP 2.41E-09 53 40 13 immune response-regulating signaling pathway BP 2.64E-09 31 29 2 cytokine-mediated signaling pathway BP 2.65E-09 33 29 4 cytokine receptor binding MF 3.12E-09 26 25 1 innate immune response BP 3.65E-09 36 34 2 cell chemotaxis, the positive regulation of immune system process, the response to other organism, the response to external biotic stimulus, the regulation of immune response, the response to biotic stimulus and the regulation of signaling receptor activity. The inflammatory response was the most affected of the subcategories, including upregulated genes IL8L1, TLR15, PPBP, CD44, ADAM8, NFKBIZ, IL8, ELF3, F3, BDKRB, down-regulated genes GPRC5B and so on. Kegg Pathway Enrichment Analysis To understand the various biological processes of GX14FF03 infection, a KEGG pathway analysis based on the SDEGs was performed. The KEGG data analysis revealed that the SDEGs are involved in eight significant pathways, included Cytokine-cytokine receptor interaction, Neuroactive ligand-receptor interaction, Toll-like receptor signaling pathway, Calcium signaling pathway, Phenylalanine metabolism, Intestinal immune network for IgA production, Cytosolic DNA-sensing pathway and Regulation of actin cytoskeleton (Fig. 2 B, Table 4. Supplementary table S3). In the top three most significantly enriched pathways, IL8L1, CSF3, CCL19, IL8, IL13RA2, CCL20, IL1B, IL6, IL17C, RELT and some immune-related genes were enriched in cytokine-cytokine receptor interaction, and BDKRB1, PTGER2, GRIN2C, GRIA4, AGTR2, BDKRB2, ADORA2B, HTR2A, EDN, HTR7 and some other likes genes were enriched in Neuroactive ligand-receptor interaction, and IL8L1, IL8, IKBKE, chTLR1-type2, IL1B, IRF7, IL6, MAP3K8, CCL4, TLR1A and some other like genes were enriched in Toll-like receptor signaling pathway. Table 4 KEGG pathway analyses of the differentially expressed genes. The first column indicates that the cell signalling pathways involved with differentially expressed genes. p-Value < 0.05 were used as the threshold to select significant pathways. Tine Name of pathway P value Gene No. Up Gene No. Down Gene No. 72h Cytokine-cytokine receptor interaction 8.86E-15 37 35 2 Neuroactive ligand-receptor interaction 1.54E-10 40 24 16 Toll-like receptor signaling pathway 2.04E-06 15 15 0 Calcium signaling pathway 0.000201252 20 14 6 Phenylalanine metabolism 0.000592564 5 3 2 Intestinal immune network for IgA production 0.001812759 6 6 0 Cytosolic DNA-sensing pathway 0.001828423 7 7 0 Regulation of actin cytoskeleton 0.003405639 18 11 7 Protein-protein Interaction Network (Ppi) Analysis Of Degs PPI network analyses identified a total of 359 interaction relationships among 480 proteins (Fig. 3 ). The SDEGs SAA, BDKRB2, BDKRB1, C3AR1, POMC, AGTR2, IL8L1, IL8L2, SSTR2, GRM3, HTR1D, GPR183, P2PY4, CCL20, CCL1 and CCL19 gene shave more than 15 interactions, and there play important roles in maintaining the connection of the whole network (Fig. 3 B, Supplementary table S4). Validation of the RNA-seq results by Quantitative real-time PCR (RT-qPCR) We randomly chose eight genes (IL6, IL10, FGF16, SMOC1, TLR15, IL8L1, MYLK, and ACOD1) for RT-qPCR investigation to confirm the consistency and repeatability of SDEGs discovered through the transcriptome sequencing. The findings revealed that these genes were highly significantly elevated and were consistently up-regulated or down-regulated with gene expression variations based on RNA-Seq (Fig. 4 ) implying that the SDEGs derived from transcriptome sequencing were credible. Discussion Viruses may infect hosts in a variety of ways [ 16 ], changing the host's cellular metabolic network to create an appropriate intracellular milieu for the viral life cycle [ 17 ]. Molecules of the host are altered to regulate viral infection, and these alterations may be reflected in the transcriptional profile [ 18 ]. For many years, research on ALV had been continuing [ 5 ]. In CEFs with the ALV-J infection, previous research revealed that 36 deferentially expressed lncRNAs and 91 genes were deferentially expressed [ 19 ]. During ALV-J infection, 228 lncRNAs were differently expressed in HD11 cells and 361 lncRNAs in CEFs, according to another research [ 20 ]. The majority of these researches are focused on ALV-J, with only a few studies focusing on the recombinant ALV. Recombinant ALV, to our knowledge, has a particular proportion in clinical situations [ 10 ]. The transcriptional alterations produced by the recombinant ALV isolate GX14FF03 infection in CEFs were investigated using RNA sequencing in the current study. At 72 hours post-infection, SDEGs were identified in CEF infected with ALV. In addition, this study revealed a total of 19,895 genes, including 907 SDEGs (P-adj 0.05 and log2FoldChangde>|2|). The RT-qPCR analysis of the expression variations of randomly selected 8 genes revealed a high connection with the RNA-Seq results. This supported the findings from the RNA-Seq analysis being reliable.. IL8L1 was the most significantly upregulated gene among 907 SDEGs. In chickens, IL8L1 is the chicken homologue of CXCL8 (also known as interleukin-8, IL8), a member of the CXC family. CXCL8, a pro-inflammatory factor, is associated to the incidence and progression of a variety of inflammatory diseases, including proliferation, angiogenic responses, tumour metastasis, and encouraging infected tissue healing [ 21 ]. Previous studies showed that IL8L1 has a causal role in establishing acute inflammation; play important role in PPI network; probably associates with vIL8, which recruits T cells to B cells infected with MDV and leads to transduction of viral signal from B to T cells [ 22 ]. IL8L1 was enriched in 15.87% (40/252) GO terms, and the rate was high as 86.67% (26/30) in the top 30 GO terms. Besides, IL8L1 was enriched in cytokine-cytokine receptor interaction and toll-like receptor signaling pathway. PPI network analyses resulted showed that IL8L1 shown interactions with IL6, CCL19, GRM3, BDKRB2, POMC, P2RY4, IL8L2, BDKRB1, GPR183, SAA, CCL20, THR1D, IL10, CCL1, C3AR1, AGTR2, and SSTR2. It is imperative to study the specific mechanism of IL8L1 involved in the recombinant ALV infected CEFs. The findings revealed eight major pathways, with the cytokine-cytokine receptor interaction signaling pathway being particularly active. Cytokines have an important role in immunological control, cell development and differentiation, and inflammation [ 23 ]. Furthermore, we were drawn to the Toll-like receptor signaling pathway. Toll-like receptor signaling pathways are critical components of innate immune responses and play a role in viral infection resistance [ 24 ]. Studies show that TLR signaling pathway might contribute to controlling HBV infection [ 25 ], substantial reductions in the mRNA expression of TLR1, TLR2, TLR3 were detected in the surviving BM-DCs following ALV-J infection [ 26 ]. In the present study, IL8L1, IL8, IKBKE, chTLR1-type2, IL1B, IRF7, IL6, MAP3K8, CCL4, TLR1A, IL-12B, and LY96 were found to be involved in the Toll-like receptor signaling pathway. However, whether the Toll-like receptor signaling pathway affects ALV-B infected CEF cells need to be further studied. Conclusions In the present study, we characterized the transcriptome profile of CEFs infected with a recombinant ALV isolate GX14FF03. We observed a highly correlated expression pattern of mRNAs induced by the recombinant ALV with coding genes that are involved in antiviral innate immunity. The dynamic changes of differentially expressed genes of GX14FF03 infection contributed to understanding the molecular mechanisms of ALV-host interaction. In the future, more research is required to elucidate the mechanisms underlying the functions of the mRNAs involved in the recombinant ALV infection. Abbreviations ALV avian leukosis virus; CEFs:chicken fibroblast cells; SDEGs:significant differentially expressed genes; IL8L1:interleukin 8-like 1; FGF16:fibroblast growth factor 16; GO:Gene Ontology; DMEM:Dulbecco’s modified eagle medium; FBS:fetal bovine serum; TCID50:50 % tissue culture infective dose; PPI:Protein-to-protein interaction; RT-qPCR:reverse transcription quantitative real-time PCR; qPCR:Quantitative real-time PCR; BP:biological progress; CC:cellular component; MF:molecular function. Declarations Acknowledgements The manuscript was kindly reviewed by Dr. Richard Roberts, Aurora, CO 80014, USA. Authors’ contributions Conceptualization: WPK, WJ conceived and designed the experiments. LQH collected the samples and data; WPK, LQH,DQM, LM performed the experiments; WPK analyzed the data and wrote the manuscript. DQM, LM reviewed and edited the manuscript. All authors read and approved the final manuscript. Funding This work was supported by the Shandong Provincial Natural Science Foundation [ZR2019BC047], the Guangxi Special Funding on Science and Technology Research [AA17204057], the Guangxi Program for Modern Agricultural Industry Technical System Construction-Chicken Industry [nycytxgxcxtd-19-03], the Major Basic Program of Natural Science Foundation of Shandong Province, China [ZR2019ZD21], the Taishan Scholars Program of Shandong Province, China [ts20190955]. Availability of data and materials The datasets used and/or analyzed in the current study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate No animal experiment was involved in this study. Consent for publication Not applicable. Competing interests The authors declare no conflict of interest for this manuscript. 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Infection, genetics and evolution 44:130–136 Supplementary Files Supplementarytable.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Minor Revision 15 Jul, 2022 Reviews received at journal 25 Mar, 2022 Reviewers invited by journal 23 Jan, 2022 Editor assigned by journal 08 Sep, 2021 First submitted to journal 07 Sep, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies 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-888600","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":78592935,"identity":"d4cd693a-a7dd-40c5-ac46-b4c69d7402b8","order_by":0,"name":"Peikun Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIie3PMUsDMRTA8RyBm3LcmmA/REogWjzsV8lRuOnQjjeIBAoZ2/X8Is45CnVJ7Ro4hxNHO1zp4iSNq2Cuo2D+4+P9eAkAodDfDVMAoNaiylCayrNJnHedKUak1uedcQSx8ZtaZ1SKgdXn7fpjXl0x2kqOhdkhCnTUH0oPMbfFdW0wp6+6wKJq0SWUkDw+/U64LjlLFM6oFRt3pUUTqWOY+Mhu78jXN8kVztULoloMEFuy90S6h9kZpLnSw2Rq9xzWG8yILaJOmBkidbPw/oWsSnac3z+Ml/aubz6rm2maLpr+4CGu+OLHIJLefRc8Dm2EQqHQP+8EmSRYT59U+okAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-8596-181X","institution":"Microbo and HosT Health","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Peikun","middleName":"","lastName":"Wang","suffix":""},{"id":78592936,"identity":"f5d6ce74-47e7-4b79-9fe0-cf93ee09a2d2","order_by":1,"name":"Qiuhong Li","email":"","orcid":"","institution":"Guangxi University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qiuhong","middleName":"","lastName":"Li","suffix":""},{"id":78592937,"identity":"81c1c10d-8c65-4998-b3a9-2d0dd93f191f","order_by":2,"name":"Jing Wang","email":"","orcid":"","institution":"Animal epidemic disease anticipatory control center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Wang","suffix":""},{"id":78592938,"identity":"00090a8c-94f0-4912-a8ce-3146c075f452","order_by":3,"name":"Qiaomu Deng","email":"","orcid":"","institution":"Guangxi University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qiaomu","middleName":"","lastName":"Deng","suffix":""},{"id":78592939,"identity":"260601e6-6b9e-445b-a6ff-21dd10ac5ad1","order_by":4,"name":"Min Li","email":"","orcid":"","institution":"Guangxi University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Li","suffix":""},{"id":78592940,"identity":"336afd72-eeee-4490-a142-9dd560d88bd2","order_by":5,"name":"Ping Wei","email":"","orcid":"","institution":"Guangxi University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2021-09-09 06:27:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-888600/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-888600/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":17637045,"identity":"6bfe7516-07fb-4383-bd92-4bce2e0989e7","added_by":"auto","created_at":"2022-01-25 15:32:36","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":78037,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene-expression profiling of CEFs upon recombinant ALV stimulation. \u003c/strong\u003e\u003c/p\u003e\u003cp\u003e(A) Volcanic plot of DEGs. The Red dot represents significantly up-regulated genes (N=534), The green dot represents significantly down-regulated genes (N=373), and blue dots represent non-differentially expressed genes (N=18988). (B) Heat-map expression profiles of 30 significantly dysregulated mRNAs between recombinant ALV-infected and uninfected CEF cells, at 72 hours after infection.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-888600/v1/6cefcd614505a1b7f5a937ee.jpg"},{"id":17637047,"identity":"9bdd96dc-25a9-4ee2-9ad3-2a2d6c159683","added_by":"auto","created_at":"2022-01-25 15:32:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":108177,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis of differentially expressed mRNAs in CEFs of different groups during the recombinant ALV infection.\u003c/strong\u003e (A) Top 30 GO terms analysis of differentially expressed mRNAs in the ALV positive group vs Control group; (B) Scatter diagram of KEGG signal pathway. Horizontal axis: rich factor, vertical axis: signal pathway. rich factor: the ratio of the number of differentially expressed genes in a signal pathway to\u0026nbsp;all annotated genes; only the top 20 gene regulation entries are displayed.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-888600/v1/9ff74fcbf6909f76ef50c49c.jpg"},{"id":17637046,"identity":"d5fd85dd-260b-48bd-8ed4-18385ce703fc","added_by":"auto","created_at":"2022-01-25 15:32:36","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":187984,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProtein-protein interaction network analysis of DEGs.\u003c/strong\u003e (A) Protein interaction relationship of selected DEGs existing in the database were extracted for the construction of network, with confidence scores between interactions greater than 0.9. Then, the visualization of the network is carried out using Cytoscape. The circle represents genes and the straight line represents the interactions. The Yellow sphere represents genes who that shave more than 15 interactions. (B) The interactions between SAA, BDKRB2, BDKRB1, C3AR1, POMC, AGTR2, IL8L1, IL8L2, SSTR2, GRM3, HTR1D, GPR183, P2PY4, CCL20, CCL1 and CCL19.\u0026nbsp;\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-888600/v1/c192ebcb0726a3354b4dbb16.jpg"},{"id":17637048,"identity":"6c890394-dd01-4ad2-bcba-34b2608c4cfa","added_by":"auto","created_at":"2022-01-25 15:32:36","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":62087,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of RNA-Seq data (for mRNAs) by real-time PCR\u003c/strong\u003e. Average fold changes of gene expression in CEF cells at 72 h following recombinant ALV, as determined by qRT-PCR and RNA-seq analysis. The expression of selected mRNAs in recombinant ALV-infected CEF cells together with the expression of mock-infected controls was validated by qRT-PCR using a pair of specific primers and a probe for each mRNA. Data are expressed as the mean ± SD of triplicate reactions for each gene transcript.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-888600/v1/3562c9435e981c0164ecde55.jpg"},{"id":17637050,"identity":"304d7f24-f300-486d-a9eb-5bc9239ad555","added_by":"auto","created_at":"2022-01-25 15:32:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":830326,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-888600/v1/096467a2-d93e-4341-b258-544a1811dce6.pdf"},{"id":17637049,"identity":"32e385b9-e5cd-4d14-b05a-a7fb904f1d00","added_by":"auto","created_at":"2022-01-25 15:32:36","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":196889,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable.docx","url":"https://assets-eu.researchsquare.com/files/rs-888600/v1/e11117bdd58175e052837db4.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eTranscription Analysis of Chicken Embryo Fibroblast Cells Infected with a Recombinant Avian Leukosis Virus Isolate GX14FF03\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eAvian leukosis virus (ALV) is a retrovirus that can cause tumorigenesis in chickens when given exogenously [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. ALV that infect chickens comprise 7 envelope subgroups designated A\u0026ndash;E, J, and K [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Among these subgroups, subgroup J, first isolated in the United Kingdom in 1988, and which later proved to have a broader neoplastic spectrum [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Subgroups A and B are classical and common pathogenic exogenous viruses that induce lymphoid leukosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite current purification strategies, ALVs remains a serious problem in the poultry industry for there is no effective means of dealing with them [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTranscriptional profiling examination of the host response to an infectious agent, which provides new insights into the interaction between the host and the infectious organism [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Several \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e investigations have revealed the ALV infected birds and cells have differently expressed genes or proteins [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, there are few studies that focused on the recombinant ALV [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In this study, RNA sequencing was performed to investigate the expression pattern of mRNAs in chicken embryo fibroblast cells infected by recombinant avian leukosis virus isolate GX14FF03.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eVirus and cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA recombinant avian leukosis virus isolate GX14FF03 (GenBank accession number KU923579) was previously isolated from a commercial native chicken in Guangxi Province\u0026nbsp;[9]. The virus \u003cem\u003er\u003c/em\u003eGX14FF03 used in this study were rescued by using reverse genetics technique and identified\u0026nbsp;[10]. Our previous research showed that this isolate caused body weight decrease and immunosuppression in experimental chickens. Chicken embryo fibroblasts (CEF) were cultured in Dulbecco\u0026rsquo;s modified eagle medium (DMEM) containing 5% fetal bovine serum (FBS) at 37\u0026nbsp;℃\u0026nbsp;and 5% CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003euntil they reached about 80% confluence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample preparation and mRNA sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe virus \u003cem\u003er\u003c/em\u003eGX14FF03 was diluted to 50 % tissue culture infective dose (TCID50) of 10\u003csup\u003e4\u003c/sup\u003e and inoculated into the CEFs for 2 h, then the cells were maintained in DMEM with 1% FBS, and harvested at 72 h of post-infection. The \u003cem\u003er\u003c/em\u003eGX14FF03 infected CEFs and control CEFs were processed for mRNA sequencing and analysis by Novoene company, Beijing, China. The Illumina Novaseq 6000 platform was used for mRNA sequencing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential expression mRNA analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferential expression mRNA\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eanalysis\u0026nbsp;was performed using the DESeq2 R package (1.20.0). Genes with an adjusted P-adj\u0026lt;0.05 and log2FoldChangde\u0026gt;|2|found by DESeq2 were assigned as differentially expressed. Gene Ontology (GO) enrichment analysis of differentially expressed genes was implemented by the ClusterProfiler R package, in which gene length bias wascorrected\u0026nbsp;[11]. GO terms with corrected p-value less than 0.05 were considered as significantly enriched.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKEGG is a database resource for understanding high-level functions and utilities of the biological system [12]. ClusterProfiler R package were used to test the statistical enrichment of differential expression genes in KEGG pathways. KEGG pathways with corrected p-value less than 0.05 were considered as significantly enriched.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProtein-to-protein interaction (PPI) network analyses of SDEGs were performed using STRING database (https://string-db.org/) [13]. PPI network visualization was created with Cytoscape (version 3.8.2), and core proteins were identified by calculating the number of interactions between each network node [14] .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative real-time PCR analysis for DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe DEGs identified through transcriptome sequencing were validated using the reverse transcription quantitative real-time PCR (RT-qPCR). For RT-qPCR validation, IL6, IL10, FGF16, SMOC1, TLR15, IL8L1, MYLK, and ACOD1 were used. Primer Express\u0026reg; software for the Quantitative real-time PCR (qPCR) was used to design target-specific primers for the randomly selected genes, as indicated in Table 1. Total RNA samples were extracted and reverse transcribed to cDNA according to the manufacturer\u0026rsquo;s guidelines of the used RNA extraction kit (TIANGN, China) and PrimeScriptTM kit (Takara, China). The final volume is 20 \u0026micro;l containing 0.4 \u0026micro;l Dye, 0.4 \u0026micro;l of each primer, 2 \u0026micro;l of cDNA, 10 \u0026micro;l SYBR and 6.8 \u0026micro;l water. Assays were performed in triplicate. Analysis was performed by the 2\u003csup\u003e-∆∆CT\u0026nbsp;\u003c/sup\u003emethod.\u003c/p\u003e\n\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimers for RT-qPCR in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimer sequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrimer sequence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL6F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAAATTCGGTACATCCTCGACGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTLR15F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAACCTGGTGCATTTGAGAACCTGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL6R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGAAGGTTCAGGTTGTTTTCTGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTLR15R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTTTCAGGTGAGGTGCAAGACCAGA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL10F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTTGCTGGAGGACTTTAAGGGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIL8L1F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCCTCACTGCAAGAATGTGGA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL10R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTTGATGTCTGGGTCTTGGTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIL8L1R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGGAGGAGGTAGGACGTTTTTG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGF16F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTGCATGGACCGGACTTTCTGTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMYLKF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAAGATTGAAGGATACCCAGAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGF16R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGTCAGCTTCTTCGACCCATAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMYLKR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTCCATCGTTTCCACAATGAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMOC1F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGCTATTAACTCAGCAGCACCTACT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACOD1F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTGCTGCTGCGTCCAAGTTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMOC1R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGTTATTGTCCAACTGGCTGAAGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACOD1R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGGGGCTTAGTCTGAGTGGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/br\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were analyzed using GraphPad Prism, version 5.0, software and are expressed as means. The one-way ANOVA analysis was used to assess differences among clusters in the Statistical Package for Social Science Windows, version 19, software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData deposition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw sequence data were deposited in the NCBI database Sequence Read Archive with the accession number PRJNA757623.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of the transcriptome sequencing data\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from GX14FF03 or mock-infected CEFs to examine gene expression. At least 6.06 Gb of clean data from each sample (39.24GB ) was obtained from the transcriptome sequencing. For all samples, the Q20 and Q30 percentages of clean data were higher than 97.50 % and 94.00 %, respectively. For all samples, the GC content of the clean data varied from 50.31 to 51.43 % (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Approximately 87.2-90.98% of clean reads were uniquely mapped to the reference \u003cem\u003eGallus gallus\u003c/em\u003e genome (Hisat2 v2.0.5. ). Through normalisation and statistical analysis, a total of 907 significant differentially expressed genes (SDEGs) were identified, including 534 genes up-regulated and 373 genes down-regulated, 822 genes were known, and 85 genes were novel (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, Supplementary table S1). At the top 30 SEDGs, 26 were up-regulated and only 4 (FGF16, NKAN4, SMOC1, MYLK ) were down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Interleukin 8-like 1 (IL8L1) was the most significantly up-regulated gene with a log2FoldChange of 7.618, while fibroblast growth factor 16 (FGF16) was the most significantly down-regulated gene with a log2FoldChange of -4.111 (Supplementary table S1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary statistics for sequence quality control and mapped data of samples\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaw\u003c/p\u003e \u003cp\u003ereads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClean\u003c/p\u003e \u003cp\u003ereads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ20\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ30\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGC pct\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003emapped\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnique\u003c/p\u003e \u003cp\u003emapped\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMulti\u003c/p\u003e \u003cp\u003emapped\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMock 72h 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47,504,588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45,787,868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40,732,727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e39,928,848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e803,879\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMock 72h 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45,575,230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44,426,782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41,121,076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e40,418,091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e702,985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMock 72h 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41,586,088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40,408,138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37,390,145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e36,755,609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e634,536\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALV-B 72h 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42,551,534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41,474,616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37,948,237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e37,233,912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e714,325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALV-B 72h 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46,824,782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45,451,720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41,871,050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e41,088,886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e782,164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALV-B 72h 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45,293,416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44,047,452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40,580,093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e39,806,401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e773,692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003c/div\u003e\u003c/br\u003e\n\u003ch2\u003eGene Ontology (Go) Analysis\u003c/h2\u003e\n\u003cp\u003eTo generally described the functions of genes obtained from RNA-Seq, functional annotations were performed by comparing the sequences with the GO databases. Genes annotated by GO database were classified into the three GO categories, namely, biological progress (BP), cellular component (CC), and molecular function (MF) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The significance of GO term enrichment in the DEGs was based on a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. A total of 907 sDGEs were enriched for 252 GO terms were significantly enriched, including 197 BP, 3 CC (cell surface, external side of plasma membrane, receptor complex) and 52 MF (Supplementary table S2). We are concerned here with the top 30 GO terms, which contained 24 BP and 6 MF. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e ). In biological progress, the primary subcategories are the inflammatory response\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFunctional analysis of DEGs using GO and showing the top 30 GO terms\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e -value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGene No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003cp\u003eGene No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003cp\u003eGene No.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003einflammatory response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.77E-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ereceptor regulator activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.96E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ereceptor ligand activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.32E-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echemokine receptor binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecytokine activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.58E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echemokine activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.22E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecell chemotaxis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.10E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epositive regulation of immune system process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.19E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eresponse to other organism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eresponse to external biotic stimulus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.31E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eregulation of immune response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.74E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eresponse to biotic stimulus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.99E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eregulation of signaling receptor activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.03E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eneutrophil migration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eneutrophil chemotaxis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.37E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eleukocyte chemotaxis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.38E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eresponse to bacterium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.57E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egranulocyte chemotaxis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.96E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egranulocyte migration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.40E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eresponse to molecule of bacterial origin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.81E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eleukocyte migration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.68E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emyeloid leukocyte migration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echemotaxis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.31E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etaxis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.44E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eactivation of immune response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.04E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecell activation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.41E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eimmune response-regulating signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.64E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecytokine-mediated signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.65E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecytokine receptor binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.12E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003einnate immune response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.65E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ecell chemotaxis, the positive regulation of immune system process, the response to other organism, the response to external biotic stimulus, the regulation of immune response, the response to biotic stimulus and the regulation of signaling receptor activity. The inflammatory response was the most affected of the subcategories, including upregulated genes IL8L1, TLR15, PPBP, CD44, ADAM8, NFKBIZ, IL8, ELF3, F3, BDKRB, down-regulated genes GPRC5B and so on.\u003c/p\u003e\n\u003ch2\u003eKegg Pathway Enrichment Analysis\u003c/h2\u003e\n\u003cp\u003eTo understand the various biological processes of GX14FF03 infection, a KEGG pathway analysis based on the SDEGs was performed. The KEGG data analysis revealed that the SDEGs are involved in eight significant pathways, included Cytokine-cytokine receptor interaction, Neuroactive ligand-receptor interaction, Toll-like receptor signaling pathway, Calcium signaling pathway, Phenylalanine metabolism, Intestinal immune network for IgA production, Cytosolic DNA-sensing pathway and Regulation of actin cytoskeleton (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, Table\u0026nbsp;4. Supplementary table S3). In the top three most significantly enriched pathways, IL8L1, CSF3, CCL19, IL8, IL13RA2, CCL20, IL1B, IL6, IL17C, RELT and some immune-related genes were enriched in cytokine-cytokine receptor interaction, and BDKRB1, PTGER2, GRIN2C, GRIA4, AGTR2, BDKRB2, ADORA2B, HTR2A, EDN, HTR7 and some other likes genes were enriched in Neuroactive ligand-receptor interaction, and IL8L1, IL8, IKBKE, chTLR1-type2, IL1B, IRF7, IL6, MAP3K8, CCL4, TLR1A and some other like genes were enriched in Toll-like receptor signaling pathway.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e KEGG pathway analyses of the differentially expressed genes. The first column indicates that the cell signalling pathways involved with differentially expressed genes. p-Value \u0026lt; 0.05 were used as the threshold to select significant pathways.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.439446366782007%\"\u003e\n \u003cp\u003eTine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.965397923875436%\"\u003e\n \u003cp\u003eName of pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.089965397923876%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.245674740484429%\"\u003e\n \u003cp\u003eGene No.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.456747404844291%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003cp\u003eGene No.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.802768166089965%\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003cp\u003eGene No.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"8\" width=\"7.439446366782007%\"\u003e\n \u003cp\u003e72h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.965397923875436%\"\u003e\n \u003cp\u003eCytokine-cytokine receptor interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.089965397923876%\"\u003e\n \u003cp\u003e8.86E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.245674740484429%\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.456747404844291%\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.802768166089965%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.177570093457945%\"\u003e\n \u003cp\u003eNeuroactive ligand-receptor interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38317757009346%\"\u003e\n \u003cp\u003e1.54E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.149532710280374%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.83177570093458%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.177570093457945%\"\u003e\n \u003cp\u003eToll-like receptor signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38317757009346%\"\u003e\n \u003cp\u003e2.04E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.149532710280374%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.83177570093458%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.177570093457945%\"\u003e\n \u003cp\u003eCalcium signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38317757009346%\"\u003e\n \u003cp\u003e0.000201252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.149532710280374%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.83177570093458%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.177570093457945%\"\u003e\n \u003cp\u003ePhenylalanine metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38317757009346%\"\u003e\n \u003cp\u003e0.000592564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.149532710280374%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.83177570093458%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.177570093457945%\"\u003e\n \u003cp\u003eIntestinal immune network for IgA production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38317757009346%\"\u003e\n \u003cp\u003e0.001812759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.149532710280374%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.83177570093458%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.177570093457945%\"\u003e\n \u003cp\u003eCytosolic DNA-sensing pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38317757009346%\"\u003e\n \u003cp\u003e0.001828423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.149532710280374%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.83177570093458%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.177570093457945%\"\u003e\n \u003cp\u003eRegulation of actin cytoskeleton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38317757009346%\"\u003e\n \u003cp\u003e0.003405639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.149532710280374%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.83177570093458%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\u003c/br\u003e\n\u003ch2\u003eProtein-protein Interaction Network (Ppi) Analysis Of Degs\u003c/h2\u003e\n\u003cp\u003ePPI network analyses identified a total of 359 interaction relationships among 480 proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The SDEGs SAA, BDKRB2, BDKRB1, C3AR1, POMC, AGTR2, IL8L1, IL8L2, SSTR2, GRM3, HTR1D, GPR183, P2PY4, CCL20, CCL1 and CCL19 gene shave more than 15 interactions, and there play important roles in maintaining the connection of the whole network (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, Supplementary table S4).\u003c/p\u003e\u003cp\u003e \u003cb\u003eValidation of the RNA-seq results by Quantitative real-time PCR (RT-qPCR)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe randomly chose eight genes (IL6, IL10, FGF16, SMOC1, TLR15, IL8L1, MYLK, and ACOD1) for RT-qPCR investigation to confirm the consistency and repeatability of SDEGs discovered through the transcriptome sequencing. The findings revealed that these genes were highly significantly elevated and were consistently up-regulated or down-regulated with gene expression variations based on RNA-Seq (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) implying that the SDEGs derived from transcriptome sequencing were credible.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eViruses may infect hosts in a variety of ways [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], changing the host's cellular metabolic network to create an appropriate intracellular milieu for the viral life cycle [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Molecules of the host are altered to regulate viral infection, and these alterations may be reflected in the transcriptional profile [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. For many years, research on ALV had been continuing [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In CEFs with the ALV-J infection, previous research revealed that 36 deferentially expressed lncRNAs and 91 genes were deferentially expressed [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. During ALV-J infection, 228 lncRNAs were differently expressed in HD11 cells and 361 lncRNAs in CEFs, according to another research [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The majority of these researches are focused on ALV-J, with only a few studies focusing on the recombinant ALV. Recombinant ALV, to our knowledge, has a particular proportion in clinical situations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe transcriptional alterations produced by the recombinant ALV isolate GX14FF03 infection in CEFs were investigated using RNA sequencing in the current study. At 72 hours post-infection, SDEGs were identified in CEF infected with ALV. In addition, this study revealed a total of 19,895 genes, including 907 SDEGs (P-adj 0.05 and log2FoldChangde\u0026gt;|2|). The RT-qPCR analysis of the expression variations of randomly selected 8 genes revealed a high connection with the RNA-Seq results. This supported the findings from the RNA-Seq analysis being reliable..\u003c/p\u003e \u003cp\u003eIL8L1 was the most significantly upregulated gene among 907 SDEGs. In chickens, IL8L1 is the chicken homologue of CXCL8 (also known as interleukin-8, IL8), a member of the CXC family. CXCL8, a pro-inflammatory factor, is associated to the incidence and progression of a variety of inflammatory diseases, including proliferation, angiogenic responses, tumour metastasis, and encouraging infected tissue healing [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Previous studies showed that IL8L1 has a causal role in establishing acute inflammation; play important role in PPI network; probably associates with vIL8, which recruits T cells to B cells infected with MDV and leads to transduction of viral signal from B to T cells [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. IL8L1 was enriched in 15.87% (40/252) GO terms, and the rate was high as 86.67% (26/30) in the top 30 GO terms. Besides, IL8L1 was enriched in cytokine-cytokine receptor interaction and toll-like receptor signaling pathway. PPI network analyses resulted showed that IL8L1 shown interactions with IL6, CCL19, GRM3, BDKRB2, POMC, P2RY4, IL8L2, BDKRB1, GPR183, SAA, CCL20, THR1D, IL10, CCL1, C3AR1, AGTR2, and SSTR2. It is imperative to study the specific mechanism of IL8L1 involved in the recombinant ALV infected CEFs.\u003c/p\u003e \u003cp\u003eThe findings revealed eight major pathways, with the cytokine-cytokine receptor interaction signaling pathway being particularly active. Cytokines have an important role in immunological control, cell development and differentiation, and inflammation [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Furthermore, we were drawn to the Toll-like receptor signaling pathway. Toll-like receptor signaling pathways are critical components of innate immune responses and play a role in viral infection resistance [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Studies show that TLR signaling pathway might contribute to controlling HBV infection [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], substantial reductions in the mRNA expression of TLR1, TLR2, TLR3 were detected in the surviving BM-DCs following ALV-J infection [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In the present study, IL8L1, IL8, IKBKE, chTLR1-type2, IL1B, IRF7, IL6, MAP3K8, CCL4, TLR1A, IL-12B, and LY96 were found to be involved in the Toll-like receptor signaling pathway. However, whether the Toll-like receptor signaling pathway affects ALV-B infected CEF cells need to be further studied.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn the present study, we characterized the transcriptome profile of CEFs infected with a recombinant ALV isolate GX14FF03. We observed a highly correlated expression pattern of mRNAs induced by the recombinant ALV with coding genes that are involved in antiviral innate immunity. The dynamic changes of differentially expressed genes of GX14FF03 infection contributed to understanding the molecular mechanisms of ALV-host interaction. In the future, more research is required to elucidate the mechanisms underlying the functions of the mRNAs involved in the recombinant ALV infection.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eavian leukosis virus; CEFs:chicken fibroblast cells; SDEGs:significant differentially expressed genes; IL8L1:interleukin 8-like 1; FGF16:fibroblast growth factor 16; GO:Gene Ontology; DMEM:Dulbecco\u0026rsquo;s modified eagle medium; FBS:fetal bovine serum; TCID50:50 % tissue culture infective dose; PPI:Protein-to-protein interaction; RT-qPCR:reverse transcription quantitative real-time PCR; qPCR:Quantitative real-time PCR; BP:biological progress; CC:cellular component; MF:molecular function.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe manuscript was kindly reviewed by Dr. Richard Roberts, Aurora, CO 80014, USA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: WPK, WJ conceived and designed the experiments. LQH collected the samples and data; WPK, LQH,DQM, LM performed the experiments; WPK analyzed the data and wrote the manuscript. DQM, LM reviewed and edited the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Shandong Provincial Natural Science Foundation [ZR2019BC047], the Guangxi Special Funding on Science and Technology Research [AA17204057], the Guangxi Program for Modern Agricultural Industry Technical System Construction-Chicken Industry [nycytxgxcxtd-19-03], the Major Basic Program of Natural Science Foundation of Shandong Province, China [ZR2019ZD21], the Taishan Scholars Program of Shandong Province, China [ts20190955].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed in the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo animal experiment was involved in this study.\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\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest for this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eDeng QM, Li M, He CW, Lu QE, Gao YL, Li QH, Shi MY, Wang PK, Wei P (2021) Genetic diversity of avian leukosis virus subgroup J (ALV-J): toward a unified phylogenetic classification and nomenclature system. 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Infection, genetics and evolution 44:130\u0026ndash;136\u003c/span\u003e\u003c/li\u003e\n\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":"archives-of-virology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arvi","sideBox":"Learn more about [Archives of Virology](https://www.springer.com/journal/705)","snPcode":"705","submissionUrl":"https://submission.nature.com/new-submission/705/3","title":"Archives of Virology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Recombinant avian leukosis virus, CEF cells, RNA-seq, Transcription","lastPublishedDoi":"10.21203/rs.3.rs-888600/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-888600/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInfection with recombinant avian leukosis virus (ALV) has previously been linked to malignancies and immunosuppression. However, the processes behind recombinant ALV's unique pathophysiology are poorly understood. The study aims to investigate the gene expression patterns of a recombinant ALV isolate GX14FF03 infected chicken fibroblast cells (CEFs) and used the RNA-Sequence technique to undertake a complete analysis of the mRNAs transcriped. As a consequence, a total of 907 significant differentially expressed genes (SDEGs) were identified. Among these SDEGs, the most significantly up-regulated gene was interleukin 8-like 1 (IL8L1), while the most significantly down-regulated gene was fibroblast growth factor 16 (FGF16). The 907 SDGEs were highly enriched (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for 252 Gene Ontology (GO) terms, including 197 BP, 3 CC, and 52 MF. According to the KEGG data analysis, SDEGs are implicated in eight significant pathways (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, PPI network analyses revealed that IL8L1 interacts with 17 genes. These findings provide light on the molecular mechanisms of the recombinant ALV infection by explaining the mRNA expression profile in CEFs infected with GX14FF03 virus.\u003c/p\u003e","manuscriptTitle":"Transcription Analysis of Chicken Embryo Fibroblast Cells Infected with a Recombinant Avian Leukosis Virus Isolate GX14FF03","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-01-25 15:32:34","doi":"10.21203/rs.3.rs-888600/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor Revision","date":"2022-07-15T07:53:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-03-25T07:03:30+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-01-23T19:03:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-09-08T12:00:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Archives of Virology","date":"2021-09-08T00:03:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"archives-of-virology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arvi","sideBox":"Learn more about [Archives of Virology](https://www.springer.com/journal/705)","snPcode":"705","submissionUrl":"https://submission.nature.com/new-submission/705/3","title":"Archives of Virology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2978df3c-cea2-44aa-8cc6-3f24b352046b","owner":[],"postedDate":"January 25th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":9966913,"name":"Virology"}],"tags":[],"updatedAt":"2022-09-01T12:15:23+00:00","versionOfRecord":[],"versionCreatedAt":"2022-01-25 15:32:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-888600","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-888600","identity":"rs-888600","version":["v1"]},"buildId":"GqpaHPwrfC8PjnIFayRh5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}