Differentially expressed non-coding RNAs induced by transmissible gastroenteritis virus potentially regulate inflammation and NF-κB pathway in porcine intestinal epithelial cell line
Transmissible gastroenteritis virus (TGEV) infection can activate NF-κB pathway in porcine intestinal epithelial cells and result in severe inflammation. Non-coding RNAs (ncRNAs) are not translated into proteins and play an important role in many biological and pathological processes such as inflammation, viral infection, and mitochondrial damage. However, whether ncRNAs participate in TGEV-induced inflammation in porcine intestinal epithelial cells is largely unknown.
In this study, the next-generation sequencing (NGS) technology was used to analyze the profiles of mRNAs, miRNAs, and circRNAs in Mock- and TGEV-infected intestinal porcine epithelial cell-jejunum 2 (IPEC-J2) cell line. A total of 523 mRNAs, 65 microRNAs (miRNAs), and 123 circular RNAs (circRNAs) were differentially expressed. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed differentially expressed mRNAs were linked to inflammation-related pathways, including NF-κB, Toll-like receptor, NOD-like receptor, Jak-STAT, TNF, and RIG-I-like receptor pathways. The interactions among mRNA, miRNA, and circRNA were analyzed. The data showed that ssc_circ_009380 and miR-22 might have interaction relationship. Dual-luciferase reporter assay confirmed that miR-22 directly bound to ssc_circ_009380. We also observed that overexpression of miR-22 led to a reduction of p-IκB-α and accumulation of p65 in nucleus in TGEV-infected IPEC-J2 cells. In contrast, inhibition of miR-22 had the opposite effects. Moreover, silencing of ssc_circ_009380 inhibited accumulation of p65 in nucleus and phosphorylation of IκB-α.
The data revealed that differentially expressed mRNAs and ncRNAs were primarily enriched in inflammation-related pathways and ssc_circ_009380 promoted activation of NF-κB pathway by binding miR-22 during TGEV-induced inflammation.
KeywordsTGEV Circular RNA miRNA ncRNA
Dulbecco’s Modified Eagle Medium (DMEM)/F-12/HAM
Fragments per kilobase of transcript per million mapped reads
Hepatitis B virus
Hour post transfection
Herpes simplex virus
Porcine intestinal epithelial cells-jejunum 2
Kyoto Encyclopedia of Genes and Genomes
Multiplicity of infection
MiRNA response elements
Pathogen recognition receptors
Retinoic acid-inducible gene I
Reads per kilobase of transcript per million mapped reads
Sodium dodecyl sulfate-polyacrylamide gel electrophoresis
Transmissible gastroenteritis virus
Transcripts per million
Transmissible gastroenteritis (TGE) is an acute viral disease, which is caused by TGEV infection and characterized by vomiting, dehydration, and severe diarrhea in pigs of all ages, especially less than 2-week-old suckling piglets. TGEV can impair porcine intestinal epithelial cells and trigger inflammatory response, mitochondrial injury, apoptosis, and complete mitophagy in IPEC-J2 . Not only TGEV, but also other coronavirus such as Severe acute respiratory syndrome (SARS), Middle east respiratory syndrome coronavirus (MERS), Porcine epidemic diarrhea virus (PEDV) can cause severe inflammation response [2, 3, 4, 5], indicating that inflammation response is a common pathological process during coronaviruses infection.
Because of not coding, ncRNAs were previously known as junk RNAs. However, increasing evidence shows that ncRNAs play important regulatory roles in many biological and pathological processes, such as inflammation, viral infection, and apoptosis [6, 7, 8, 9, 10, 11, 12, 13, 14, 15]. We previously reported that TGEV infection led to 21 differentially expressed miRNAs, among which miR-4331 inhibited transcription of TGEV gene7 via targeting CDCA7 , and miR-27b functioned as a negative regulator during TGEV-induced apoptosis . Nevertheless, little is known about whether circRNAs are involved in TGEV-induced inflammation in porcine intestinal epithelial cells.
Except for directly regulating genes, circRNAs may act as regulators by competing together with miRNAs to affect the stability of target RNAs or their translation. circRNAs harboring miRNA response elements (MREs) can function as miRNAs sponge to regulate gene expression. To investigate the effects of circRNAs during TGEV-induced inflammation, we performed RNA sequencing using NGS technology to test the alteration of mRNA, miRNA, and circRNA profiles during TGEV-induced inflammation in IPEC-J2 and conducted bioinformatics analysis of differentially expressed mRNAs, miRNAs, and circRNAs. We demonstrated that differentially expressed mRNAs, miRNAs, and circRNAs were mainly enriched in inflammation and immune response. ssc_circ_009380 attenuated TGEV-induced activation of NF-κB pathway via binding miR-22.
TGEV infection induces inflammatory response in IPEC-J2
Sequencing and analysis of ncRNAs and mRNAs of TGEV-infected IPEC-J2
KEGG analysis of differentially expressed miRNAs, circRNAs, and mRNAs
Analysis of ncRNA-mRNA regulatory network
Validation of ncRNAs and mRNAs by qRT-PCR
miR-22 directly binds to ssc_circ_009380
To evaluate the effect of ssc_circ_009380 on TGEV-induced activation of NF-κB, IPEC-J2 cells were transfected with siRNA of ssc_circ_009380 (siCirc009380) (or negative control) and subsequently infected with TGEV at 1 MOI for 24 h. The ssc_circ_009380 level was down-regulated by siCirc009380 (Fig. 8c). p-IκB-α and p65 in nucleus were decreased by siCirc009380 and not affected in cytoplasm (Fig. 8f).
In the present study, differentially expressed 523 mRNAs, 65 miRNAs, and 123 circRNAs were obtained. KEGG analysis showed that differentially expressed mRNAs and ncRNAs were primarily enriched in inflammation-related pathways. circRNA ssc_circ_009380 was identified as the sponge of miR-22 and enhanced activation of NF-κB pathway through binding miR-22 during TGEV infection.
During viral infection, host pathogen recognition receptors (PRRs), including Toll-like receptor, RIG-I-like receptor, NOD-like receptor, are responsible for recognizing viruses. Then, the recognition initiates a series of signaling cascades, including Jak-STAT, TNF, and NF-κB pathways to induce antiviral response . In this study, the 523 differentially expressed mRNAs are enriched in Toll-like receptors, RIG-I-like receptors, TNF, NOD-like receptors, Jak-STAT, and NF-κB signaling pathways. NF-κB pathway is a crucial pathway in inflammatory process and can be activated by Jak-STAT, TNF, Toll-like receptor, and RIG-I-like receptor pathways, which can be activated by TGEV infection [21, 22, 23, 24, 25, 26, 27]. RIG-I is a dsRNA helicase enzyme encoded by the DDX58 gene and recognizes viral double-stranded (ds) RNA to interfere with viral infection [28, 29]. Recently, it has been confirmed that TGEV infection significantly upregulates RIG-I and activates NF-κB pathway . Toll-like receptors (TLRs) are a class of proteins and play a key role in innate immune response by recognizing viral component . TLR3, a member of TLR family, has a fundamental role in recognizing dsRNA of viruses and activation of IRF3 and NF-κB pathway . In addition, other viruses, such as PSV and Influenza A virus, also give rise to activation of NF-κB pathway via recognition of viral RNA by TLR3 [32, 33]. Consistently, we find TGEV infection upregulates RIG-I and TLR3 and induces the activation of NF-κB pathway in IPEC-J2 cells, indicating TGEV may activate NF-κB pathway via RIG-I and TLR3 signaling. Jak-STAT signaling modulates transmission of signal from cell-membrane receptors to the nucleus and is essential for cytokines and growth factors, which cause critical cellular events such as immune responses, cell death, antiviral response . It is found that Jak-STAT pathway induced p65 phosphorylation and activation of NF-κB signaling during TGEV infection [26, 35]. In this study, TGEV infection activates the Jak-STAT signaling pathway in IPEC-J2 cells, consistent with previous studies. IL-6 is a key mediator in response to acute inflammation caused by viral infection, including TGEV, PEDV, Head strong violence (HSV), H1N1 influenza virus, and hepatitis B virus (HBV) [36, 37, 38, 39]. Here, we have observed that TGEV infection up-regulates cytokines IL-6, TNF-α, IL-8, and CCL5. Extensive evidence support the conclusion [27, 40, 41]. IL-6 is both a proinflammatory cytokine and an anti-inflammatory cytokine. IL-6 induces Jak-STAT signaling pathway and subsequently activates NF-KB signaling by binding to IL6R. Moreover, activation of NF-KB can promote transcription of IL-6 [41, 42].
miRNAs and circular RNAs are crucial non-coding RNAs and implicated in various biological and pathological processes such as viral infection, immune responses, and inflammation. It was reported that cellular miRNA profiles were altered during TGEV infection in PK-15 cells and ST cells [16, 43]. In this study, miRNAs profile and circRNAs profile were changed during TGEV infection in IPEC-J2 cell line. In comparison with the miRNAs profile of ST cells and PK-15 cells infected with TGEV, we observed a distinct miRNAs profile, indicating miRNAs are tissue specific. miR-22 is involved in inhibition of myocardial ischemia-reperfusion injury by an anti-inflammation mechanism . We demonstrated that miR-22 was up-regulated by TGEV infection and had an anti-inflammation effect by targeting IL-6 and restraining DDX58, and CCL5, which is correlated with previous reports, but through different mechanisms. circRNAs are an abundant class of endogenous ncRNAs and function as a regulator via various mechanisms such as miRNA sponge, binding to RNA-binding protein, and regulating transcription and translation of gene [44, 45, 46]. We found that miR-22 could be directly attached to ssc_circ_9380 and mediate TGEV-induced inflammation response, suggesting ssc_circ_9380 acts as the sponge of miR-22.
In the present study, we performed the next generation sequencing to obtain the profiles of mRNAs, miRNAs, and circRNAs of IPEC-J2 cells during TGEV infection and investigated the effects of differentially expressed ncRNAs on TGEV induced inflammatory response. The data reveal that the differentially expressed mRNAs and ncRNAs are enriched in inflammation-related pathways and ssc_circ_009380 potentiate TGEV-induced activation of NF-κB pathway through attaching to miR-22.
Antibodies, cells, and virus
Histone H3 monoclonal antibody, phospho-I-κB monoclonal antibody, and NF-κB p65 (L8F6) mouse monoclonal antibody were purchased from Cell Signaling Technology (US). β-actin monoclonal antibody was purchased from Santa Cruz (US). Horseradish peroxidase (HRP)-conjugated secondary antibody was purchased from Pierce (US). Dylight594-conjugated secondary antibody was purchased from Genshare Biological (China). IPEC-J2 cell line was kindly gifted by Dr. Zhanyong Wei (Henan Agricultural University, China). Cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM)/F-12/HAM (Thermo Fisher Scientific, US) supplemented with 100 IU of penicillin and 100 mg of streptomycin per ml, at 37 °C in an incubator with 5% CO2. The TGEV Shaanxi strain was separated from TGEV-infected piglets . miR-22 mimics, miRNA mimics control, miR-22 inhibitors, miRNA inhibitors control, siCirc009380, and negative control were synthesized by GenePharma (China) (The sequences were shown in Additional file 1: Table S8).
Strand-specific library construction and sequencing of mRNA and circRNA
IPEC-J2 cells were infected with TGEV at 1 MOI for 24 h (indicated by T1 and T2). Meanwhile, the mock infection (indicated by M1 and M2) was carried out. Total RNA was extracted with Trizol reagent (Invitrogen, Carlsbad, CA, US). Then rRNAs were removed to retain mRNAs and ncRNAs. The mRNAs and ncRNAs were fragmented into short fragments using fragmentation buffer and reversely transcribed into cDNA using M-MLV kit (Invitrogen, US). Second-strand cDNA were synthesized by DNA polymerase I, RNase H, dNTP (dUTP instead of dTTP) and buffer. Next, the cDNA fragments were purified with QiaQuick PCR extraction kit, end repaired, poly(A) added, and ligated to Illumina sequencing adapters. Then UNG (Uracil-N-Glycosylase) was used to digest the second-strand cDNA. The digested products were separated using agarose gel electrophoresis, amplified through PCR, and sequenced using Illumina HiSeqTM 2500 by Gene Denovo Biotechnology Co. (Guangzhou, China).
Alignment with reference genome
To get high quality clean reads, reads containing adapters, low quality reads, and rRNA reads were removed and then mapped to Sus scrofa reference genome (Sus scrofa 10.2) by TopHat2 (version 188.8.131.52) with default options.
The reconstruction of transcripts was carried out using Cufflinks (V2.2.1). The program reference annotation-based transcripts (RABT) was preferred. Cufflinks constructed faux reads according to reference to make up for the influence of low coverage sequencing. During the last step of assembly, all of the reassembled fragments were aligned with reference genes and then similar fragments were removed. Then we used Cuffmerge to merge transcripts from different replicates of a group into a comprehensive set of transcripts, and then merged the transcripts from multiple groups into a finally comprehensive set of transcripts for further downstream differential expression analysis.
Identification and annotations for novel transcripts
To identify the new transcripts, all of the reconstructed transcripts were aligned with reference genome and divided into twelve categories using Cuffcompare (V2.2.1). We used the following parameters to identify reliable novel transcripts: the length of transcript was longer than 200 bp and the exon number was more than 2. The novel transcripts were then searched against KEGG database for annotations (http://www.genome.jp/kegg/).
Quantification of transcript abundance
C, the number of fragments that are mapped to transcripts; N, the total number of fragments that are mapped to reference genes; L, the number of base pairs of transcript.
Identification and statistics of circRNAs
The Anchors reads were mapped to Sus scrofa reference genome, then subjected to find_circ (V1.0) to identify circRNAs. The identified circRNAs were subjected to statistical analysis of type, chromosome distribution, and length distribution.
Quantification of circRNA abundance
C,the number of reads that were mapped to transcripts; N, the total number of reads that were mapped to reference genes; L, the number of base pairs of transcripts.
Library construction and sequencing of miRNAs
After total RNA was extracted using TRIzol, the RNA molecules in a size range of 18~ 30 nt were enriched by polyacrylamide gel electrophoresis (PAGE). Then the 3′ adapters were added. The 36~ 44-nt RNAs were enriched. The 5′ adapters were then ligated to the RNAs as well. The ligation products were reversely transcribed by PCR amplification. The 140~ 160-bp PCR products were enriched to generate a cDNA library and sequenced using Illumina HiSeqTM 2500 by Gene Denovo Biotechnology Co. (Guangzhou, China).
Alignment of miRNAs
To get clean tags, we removed low quality reads. All of the clean tags were aligned with small RNAs in GeneBank database (Release 209.0) and Rfam database (11.0) to identify and remove rRNA, scRNA, snoRNA, snRNA, and tRNA. Then, the rest of clean tags was aligned with Sus scrofa reference genome and searched against miRBase database (Release 21) to identify existed miRNAs. Unmapped miRNAs were aligned with other species. All of the unannotated tags were aligned with Sus scrofa reference genome. According to the genome positions and hairpin structures predicted by software Mireap (v0.2), the novel miRNA candidates were obaineds.
Quantification of miRNA abundance
Prediction of miRNAs targets
RNAhybrid (v2.1.2) + svm_light (v6.01), Miranda (v3.3a), and TargetScan (v7.0) were used to predict targets of miRNAs. The intersection of the targets was chosen as candidate targets of miRNAs.
Significance analysis of miRNAs, mRNAs, and circRNAs
The edgeR package (http://www.r-project.org/) was used to identify differentially expressed miRNAs, mRNAs, and circRNAs. A fold change ≥2 and ≤ 0.5, plus a P value < 0.05 were set as thresholds for significant differentially expressed miRNAs and circRNAs. mRNAs with a fold change ≥2 and ≤ 0.5, plus a false discovery rate (FDR) < 0.05, were identified as significant differentially mRNAs.
KEGG and interaction analysis of differentially expressed mRNAs, miRNAs, and circRNAs
N,the number of all genes with KEGG annotation; N, the number of target genes (differentially expressed mRNAs, target genes of differentially expressed miRNAs, or source genes of differentially expressed circRNAs); M, the number of all genes in specific pathways. m; the number of target genes in specific pathways.
p-value ≤0.05 is set as a threshold. The interaction networks among miRNAs, circRNAs, and mRNAs were built and visualized using Cytoscape (v3.5.1) (http://www.cytoscape.org/).
Both divergent primers and convergent primers were designed to identify the circular form. Head-to-tail splicing was validated by PCR and sequencing after reverse transcription.
Quantification of miRNAs, circRNAs, and mRNAs using qRT-PCR
The total RNA of IPEC-J2 cells were isolated using TRIzol reagent and reversely transcribed using M-MLV reverse transcriptase (Invitrogen, US) according to the manufacturer′s instructions. For reverse transcription of circRNAs, the total RNA was treated with RNase R (Epicentre, US) to remove linear RNA and then was subjected to reverse transcription. qRT-PCR was performed on iQ5 real-time PCR System (Bio-Rad, US) . The primers are shown in Additional file 1: Table S9. Relative quantification of miRNAs was normalized to U6 using 2-ΔΔCt method. The relative quantification of mRNAs and circRNAs were normalized to GAPDH using the 2-ΔΔCt method.
The sequences of circRNAs and 3′ UTR of mRNAs were respectively amplified by PCR and cloned into vector psiCHECK-2 (Promega, US) (Primers are shown in Additional file 1: Table S9). The binding sites of miR-22 in targets and circRNA were mutated  (Additional file 1: Table S9). IPEC-J2 cells were co-transfected with 100 ng of plasmid and 100 nM of miR-22 mimics (or miRNA mimics control, miR-22 inhibitors, miRNA inhibitors control) using Lipofectamine 3000 (Invitrogen, US). At 48 h post transfection (hpt), the luciferase activities were tested using Dual-Glo Luciferase Assay System (Promega, US) following the manufacturer′s manual.
Western blot analysis
Cells were treated with RIPA lysis buffer containing phenylmethyl sulfonylfluoride (PMSF). Protein concentration was measured using BCA Protein Assay Reagent (Pierce, US). Proteins were separated on a 12% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and subsequently transferred onto a polyvinylidene difluoride (PVDF) membranes (Millipore, US). The PVDF membrane was blocked with 5% non-fat milk for 2 h at room temperature and then subsequently incubated with primary antibodies overnight at 4 °C and HRP-conjugated secondary antibody at room temperature for 1 h. Finally, the membrane was developed with enhanced chemiluminescence (ECL) (Promega, US).
The data are presented as the means ± SEM. Statistical significance were analyzed by unpaired Student′s t-test. p < 0.05 was defined as statistical significance.
We thank Zhanyong Wei for gifting IPEC-J2 cell line.
This work was supported by National Natural Science Foundation of China (Grant No. 31472167), China Postdoctoral Science Foundation (Grant No. 2015 M570860), Key Research and Development Project in Shaanxi Province (Grant No. 2018ZDXM-NY-064), Central Project of Major Agricultural Technology Promotion Funds (Grant No. K3360217060), and Science and Technology Planning Project of Yangling demonstration zone (Grant No. 2018NY-35).
Availability of data and materials
All the sequencing data supporting the results in the current study have been deposited in NCBI with the accession number SRP128150 (https://www.ncbi.nlm.nih.gov/sra/SRP128150).
Consent to publish
XM performed the experiments, analyzed the data, wrote the paper, prepared figures and/or Tables. XZ conceived and designed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper. ZZ was involved in drafting and revising the manuscript. JG and MM performed the experiments and analyzed the data. JL and LG was involved in drafting the manuscript and prepared figures and/or Tables. YH involved in revising the manuscript. DT conceived and designed the experiments, contributed reagents/materials/analysis tools, reviewed drafts of the paper. All authors have read and approved the manuscript.
Ethics approval and consent to participate
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
- 21.Hartman ZC, Poage GM, den Hollander P, Tsimelzon A, Hill J, Panupinthu N, Zhang Y, Mazumdar A, Hilsenbeck SG, Mills GB, et al. Growth of triple-negative breast cancer cells relies upon coordinate autocrine expression of the proinflammatory cytokines IL-6 and IL-8. Cancer Res. 2013;73(11):3470–80.CrossRefGoogle Scholar
- 24.Chen S, Zhu J, Chen G, Zuo S, Zhang J, Chen Z, Wang X, Li J, Liu Y, Wang P. 1,25-Dihydroxyvitamin D3 preserves intestinal epithelial barrier function from TNF-alpha induced injury via suppression of NF-kB p65 mediated MLCK-P-MLC signaling pathway. Biochem Biophys Res Commun. 2015;460(3):873–8.CrossRefGoogle Scholar
- 25.De Simone V, Franze E, Ronchetti G, Colantoni A, Fantini MC, Di Fusco D, Sica GS, Sileri P, MacDonald TT, Pallone F, et al. Th17-type cytokines, IL-6 and TNF-alpha synergistically activate STAT3 and NF-kB to promote colorectal cancer cell growth. Oncogene. 2015;34(27):3493–503.CrossRefGoogle Scholar
- 32.Lan D, Tang C, Yue H, Sun H, Cui L, Hua X, Li J. Microarray analysis of differentially expressed transcripts in porcine intestinal epithelial cells (IPEC-J2) infected with porcine sapelovirus as a model to study innate immune responses to enteric viruses. Arch Virol. 2013;158(7):1467–75.CrossRefGoogle Scholar
- 34.Ahmad SF, Ansari MA, Zoheir KM, Bakheet SA, Korashy HM, Nadeem A, Ashour AE, Attia SM. Regulation of TNF-alpha and NF-kappaB activation through the JAK/STAT signaling pathway downstream of histamine 4 receptor in a rat model of LPS-induced joint inflammation. Immunobiology. 2015;220(7):889–98.CrossRefGoogle Scholar
- 42.Hartman ZC, Poage GM, den Hollander P, Tsimelzon A, Hill J, Panupinthu N, Zhang Y, Mazumdar A, Hilsenbeck SG, Mills GB, et al. Growth of triple-negative breast cancer cells relies upon coordinate autocrine expression of the pro-inflammatory cytokines IL-6 and IL-8. Cancer research. 2013;73(11). https://doi.org/10.1158/0008-5472.CAN-1112-4524-T.
- 47.Ding L, Chen GD, Xu XG, Tong DW. Isolation and identification of porcine transmissible gastroenteritis virus Shaanxi strain and sequence analysis of its. N gene Chin J Vet Med. 2011;47(10):09–11.Google Scholar
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.