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Open Access, Peer-reviewed
pISSN 1229-845X
eISSN 1976-555X
    J Vet Sci. 2023 Sep;24(5):e73. English.
    Published online Sep 06, 2023.
    https://doi.org/10.4142/jvs.23141
    © 2023 The Korean Society of Veterinary Science
    Original Article

    Analysis of miRNA expression in the trachea of Ri chicken infected with the highly pathogenic avian influenza H5N1 virus

    Suyeon Kang,1 Thi Hao Vu,1 Jubi Heo,1 Chaeeun Kim,1 Hyun S. Lillehoj,2 and Yeong Ho Hong1
      • 1Department of Animal Science and Technology, Chung-Ang University, Anseong 17546, Korea.
      • 2Animal Biosciences and Biotechnology Laboratory, Agricultural Research Services, United States Department of Agriculture, Beltsville, MD 20705, USA.
    • Corresponding author: Yeong Ho Hong. Department of Animal Science and Technology, Chung-Ang University, 4726 Seodong-daero, Daedeok-myeon, Anseong 17546, Korea. Email: yhong@cau.ac.kr
    Received May 27, 2023; Revised July 13, 2023; Accepted August 17, 2023.

    This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

    Abstract

    Background

    Highly pathogenic avian influenza virus (HPAIV) is considered a global threat to both human health and the poultry industry. MicroRNAs (miRNA) can modulate the immune system by affecting gene expression patterns in HPAIV-infected chickens.

    Objectives

    To gain further insights into the role of miRNAs in immune responses against H5N1 infection, as well as the development of strategies for breeding disease-resistant chickens, we characterized miRNA expression patterns in tracheal tissues from H5N1-infected Ri chickens.

    Methods

    miRNAs expression was analyzed from two H5N1-infected Ri chicken lines using small RNA sequencing. The target genes of differentially expressed (DE) miRNAs were predicted using miRDB. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were then conducted. Furthermore, using quantitative real-time polymerase chain reaction, we validated the expression levels of DE miRNAs (miR-22-3p, miR-146b-3p, miR-27b-3p, miR-128-3p, miR-2188-5p, miR-451, miR-205a, miR-203a, miR-21-3p, and miR-200a-3p) from all comparisons and their immune-related target genes.

    Results

    A total of 53 miRNAs were significantly expressed in the infection samples of the resistant compared to the susceptible line. Network analyses between the DE miRNAs and target genes revealed that DE miRNAs may regulate the expression of target genes involved in the transforming growth factor-beta, mitogen-activated protein kinase, and Toll-like receptor signaling pathways, all of which are related to influenza A virus progression.

    Conclusions

    Collectively, our results provided novel insights into the miRNA expression patterns of tracheal tissues from H5N1-infected Ri chickens. More importantly, our findings offer insights into the relationship between miRNA and immune-related target genes and the role of miRNA in HPAIV infections in chickens.

    Keywords
    Influenza A virus; immunity; network analysis; signal transduction; high-throughput rna sequencing

    INTRODUCTION

    Avian influenza viruses (AIVs) are type A influenza viruses belonging to the family Orthomyxoviridae [1]. AIV is classified as a highly pathogenic avian influenza virus (HPAIV) and a low pathogenic avian influenza virus (LPAIV) based on pathogenicity tests and systemic versus local replication [2]. H5N1, a subtype of HPAIV, is considered a global threat to the poultry industry and human health [3]. LPAIV induces mild symptoms, such as a reduction in egg production, whereas HPAIV causes high mortality rates in chickens, resulting in substantial economic losses [4]. The incidence of cases of human infection by HPAIV, H5N1, has been steadily increasing over the past 20 years and the World Health Organization (WHO) reported that 455 out of 861 people infected with this virus died in 2019 [5].

    MicroRNAs (miRNAs) are a small non-coding RNAs with a length of approximately 22 nucleotides [6]. miRNAs can post-transcriptionally regulate gene expression via mRNA degradation and translational repression [7]. By modulating the expression of genes related with the immune response, miRNAs may play regulatory roles in the immune system [8]. Moreover, specific miRNAs can control various immune-related pathways such as the Toll-like receptor (TLR) signaling pathway, mitogen-activated protein kinase (MAPK) signaling pathway, and PI3K-Akt signaling pathway [9]. Therefore, characterizing the mechanisms through which AIV impacts the expression of miRNAs that may control the immune response in chickens is crucial.

    Several studies have performed small RNA sequencing (RNA-seq) in lung and immune organs such as the thymus, bursa of Fabricius, and spleen of H5N1-infected chickens [10, 11, 12]. Moreover, we previously conducted RNA-seq analyses of lung and trachea tissues of H5N1-infected Ri chicken [13, 14]. In this study, we analyzed the expression of miRNAs and their target genes in tracheae from two genetically distinct Ri chicken lines infected with HPAIV H5N1, after which bioinformatic analyses were conducted to identify differentially expressed (DE) miRNAs and their target genes.

    MATERIALS AND METHODS

    Animals and infection

    Animal experiments were conducted with a total of forty 4-week-old specific-pathogen-free Ri chickens, out of which 20 chickens were from an HPAIV-resistant line (MX genotype) and the remaining 20 chickens were from a susceptible line (BF2 genotype) (Supplementary Table 1). Chickens with allele A at position 631 of Mx were selected for the resistant line, whereas those with allele G at the same position were selected for the susceptible line (Supplementary Fig. 1). Furthermore, based on the BF2 genotyping, chickens exhibiting the B21 haplotype were classified as resistant, whereas the chickens with the B13 haplotype were classified as susceptible [14]. Therefore, Mx(A)/B21 chickens were classified as resistant and Mx(G)/B13 chickens were classified as susceptible.

    A total of 20 Ri chickens from each line were intranasally inoculated with 200 μL of the harvested allantoic fluid of infected eggs that contained 1 × 104 titers of the A/duck/Vietnam/QB1207/2012, which was determined to be the egg infectious dose 50 for H5N1 [15]. The HPAIV challenge experiments were performed at the Department of Biochemistry and Immunology of the National Institute of Veterinary Research (NIVR), Vietnam, according to the Office International des Epizooties guidelines.

    Trachea collection and total RNA extraction

    Trachea samples were collected from euthanized chickens at 1 day post-infection (dpi) and 3 dpi, as recommended by the WHO manual on animal influenza diagnosis and surveillance. All animal protocols were approved by the NIVR, Vietnam (TCVN 8402:2010/TCVN 8400–26:2014).

    All trachea samples were homogenized via cryogenic grinding and total RNA was extracted using the TRIzol reagent (Invitrogen, USA) according to the manufacturer’s instructions.

    Small RNA-seq and sequencing data analysis

    The tracheal RNA quality was assessed using a Bioanalyzer RNA chip (Agilent Technologies, USA) and a Trinean DropSense96 micro-sample UV/VIS spectrophotometer (Trinean, Gentbrugge, Belgium). The samples that did not meet the QC and RIN value quality criteria (28S:18S > 1 and RIN > 7) were excluded from sequencing. A library was constructed using the TruSeq Small RNA Library Prep Set (Illumina, USA). Small RNA-seq was performed by LAS (Korea) using an Illumina NextSeq 500 system.

    After sequencing, the raw reads were filtered using FastQC v0.11.5 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Skewer v0.2.2 (https://github.com/relipmoc/skewer/) was used for trimming the raw quality bases and sequencing adaptors. The processed reads were mapped to the reference genome (gg6) using QuickMIRSeq (https://sourceforge.net/projects/quickmirseq/files/). All known mature miRNA sequences were obtained from the miRbase database [16]. The expression level of miRNAs was calculated as reads per million reads. DE miRNAs were identified via statistical analysis using edgeR (Empirical Analysis of Digital Gene Expression Data in R).

    Next, the target genes of the DE miRNAs were predicted using the miRDB MicroRNA Target Prediction Database (https://mirdb.org/) [17, 18]. As recommended by the curators of the miRDB database, the target genes with target scores > 80 were used for Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis using GO resource (http://geneontology.org/) and David 2021 (https://david.ncifcrf.gov/) [19]. The results of our GO and KEGG pathway analyses were visualized using R [20]. The network between DE miRNAs and target genes related to immune response pathways was visualized using Cytoscape 3.9.0 [21].

    Primer design

    To validate the sequencing results, the known mature miRNA sequences obtained from miRBase v22.1 [16] were used for the forward primer, whereas the mRQ 3’-primer of the Mir-X miRNA qRT-PCR TB Green kit (TaKaRa Bio, Japan) was used as the reverse primer. Next, gene-specific primers were designed using Primer-BLAST (https://www.ncbi.nlm.nih.gov/tools/primer-blast/) to validate the expression level of the target genes of DE miRNAs. All primers were synthesized by Genotech (Korea) (Table 1).

    Table 1
    Primers used for quantitative real-time polymerase chain reaction

    Validation of expression of miRNA and target genes by quantitative real-time polymerase chain reaction (qRT-PCR)

    cDNA for miRNA expression analysis was synthesized using the Mir-X miRNA First-Strand Synthesis kit (TaKaRa Bio) according to the manufacturer’s instructions. The expression values of the miRNAs were determined using the Mir-X miRNA qRT-PCR TB Green kit (TaKaRa Bio) using a CFX Connect Real-Time PCR Detection System (Bio-Rad, USA) according to the manufacturer’s recommendations. U1A was used as a reference gene for the assessment of relative differential expression.

    To quantify the expression level of target genes, cDNA was synthesized using the Revert Aid First Strand cDNA Synthesis kit (Thermo Fisher Scientific, USA) according to the manufacturer’s instructions. Briefly, 2 μg of total RNA was combined with 2 μL of DNase I, 1 μL of reaction buffer (10×), and nuclease-free water up to 10 μL. The mixture was then incubated at 37°C for 30 min. Afterward, the mixture was mixed with 1 μL of EDTA and incubated at 65°C for 10 min. Next, 4 μL of reaction buffer (5×), 1 μL of OligodT, 2 μL of dNTP (10 mM), 1 μL of RiboLock RNase Inhibitor (20 U/μL), and 1 μL of RevertAid M-MuLV RT (200 U/μL) were added to the mixture. The reaction mix was incubated at 42°C for 60 min and 72°C for 5 min. Then, 130 μL of nuclease-free water was added to the reverse-transcription reaction mixtures for dilution. Target gene expression level was determined using Dyne qPCR 2X PreMIX (Dyne Bio, Korea) in a CFX Connect Real-Time PCR Detection System (Bio-Rad) following the manufacturer’s instructions. GAPDH was used as a reference gene. All qRT-PCR assays were conducted in triplicate. miRNA and target gene differential expression were calculated using the 2−△△Ct method [22].

    Statistical analysis

    Statistical analyses were performed using the SPSS software (version 25.0; IBM, USA). All data were expressed as the mean ± standard error of the mean. Student’s t-test was conducted to identify unpaired differences in expression between the experimental groups. The p < 0.05 was considered statistically significant.

    RESULTS

    Analysis of small RNA-seq

    Small RNA-seq was performed to determine the miRNA expression in the control and H5N1-infected Ri chickens. A total of 24 trachea RNA samples at 1 dpi and 3 dpi that passed the quality criteria were used for small RNA-seq analysis. The infected chickens exhibited most of the typical symptoms of HPAIV at 3 dpi and 11 of these 3 dpi samples were used for analyzing the small RNA-seq. The results of the read quality check are summarized in Supplementary Table 2. In the susceptible line, 39.1 million raw read pairs were obtained in the control group and 41.3–46.5 million read pairs were obtained in the infected group (Supplementary Table 2). A total of 35.5 million clean read pairs were obtained for the control, whereas 37.0–43.6 million clean read pairs were obtained for the infection group at 3 dpi (Supplementary Table 2). The ratio of the raw and clean read base pairs with a Phred quality score of ≥ 30 (Q30) exceeded 80% for all of the examined sequences (Supplementary Table 2). A total of 36.1–36.6 million raw read pairs were obtained before processing. After processing, the clean read pairs ranged from 31.7 to 37.1 million in the resistant line (Supplementary Table 2).

    The ratio of miRNAs accounted for an average of 66% in the control and 59% in the resistant line (Table 2). The ratio of miRNAs accounted for 65% in the control and 73% in the susceptible line (Table 2).

    Table 2
    Distribution of small RNA sequences from trachea samples of the control and H5N1-infected Ri chickens

    Next, the clean reads were aligned to the reference genome (gg6). A total of 472 mature miRNAs were mapped to the reference genome in both the susceptible and resistant lines. In the susceptible line, 152 miRNAs were identified as DE in the infection group, with DE sequences being defined as those with a |log2fold change| ≥ 1 and p < 0.05 (Fig. 1A, Supplementary Table 3). A total of 81 miRNAs were downregulated and 71 miRNAs were upregulated in the infection group compared with the control group (Fig. 1A, Supplementary Table 3). Particularly, gga-miR-6640-5p exhibited the lowest log2 fold change (−5.88-fold), whereas gga-miR-1684b-3p exhibited the highest log2 fold change (−10.15-fold) (Supplementary Table 3). A total of 46 DE miRNAs were identified in the resistant line, among which 21 miRNAs were downregulated and 25 miRNAs were upregulated in the infection samples compared with the control (Fig. 1B, Supplementary Table 3). From these samples, gga-miR-1755 exhibited the lowest log2 fold change (−2.79-fold), whereas gga-miR-1682 showed the highest log2 fold change (5.42-fold) (Supplementary Table 3). A total of 53 DE miRNAs were identified in the infection group of the resistant line compared to the infection group of the susceptible line (Fig. 1C, Supplementary Table 3). In this comparison, gga-miR-1684b-3p exhibited the lowest log2 fold change (−2.62-fold), whereas gga-miR-7b showed the highest log2 fold change (9.53-fold) (Supplementary Table 3).

    Fig. 1
    Volcano plot and hierarchical clustering analysis of differentially expressed miRNAs in Ri chickens. (A-C) Volcano plot: (A) susceptible line, (B) resistant line, (C) comparison between the infection groups of the resistant and susceptible Ri chicken lines. X-axis: log2 fold change; Y-axis: -log10 p value. The blue dots indicate downregulated DE miRNAs and the red dots indicate upregulated DE miRNAs with |log2fold change| ≥ 1 and p < 0.05.
    DE, differentially expressed; miRNA, microRNA.

    In previous study, gene expression patterns on trachea from H5N1-infected Ri chicken were analyzed by RNA-seq [13]. Few DE miRNAs in infection samples of resistant line and susceptible line were showed negative correlation with DE genes from RNA-seq result that conducted with same tracheal RNA samples (Supplementary Table 4). A total of 5 DE miRNAs were down-regulated and 10 target genes were up-regulated in infection group of the resistant line compared with the susceptible line (Supplementary Table 4). A total of 38 up-regulated DE miRNAs demonstrated negative correlation with 139 target genes that up-regulated in infection samples of the resistant line against the susceptible line (Supplementary Table 4).

    Target genes prediction and bioinformatic analysis

    Next, miRDB (https://mirdb.org/) was used to predict the target genes of the 53 miRNAs that were identified as DE in the resistant line compared with the susceptible line in the infection samples. A total of 3,755 target genes with scores higher than 80 were predicted from 53 DE miRNAs (Supplementary Table 5). Bioinformatic analyses were performed using the 3,755 target genes and the top 30 terms with a false discovery rate (FDR) < 0.05 and fold enrichment > 1 were illustrated in Fig. 2. Particularly, the Biological Process GO category mainly included neuron development, cell morphogenesis, and neuron projection development (Fig. 2A). The molecular function (MF) terms were mainly associated with MF, binding, and ion binding (Fig. 2B). The cellular component category mainly included pathways associated with the nucleus, plasma membrane, and the endomembrane system (Fig. 2C). To understand the biological functions of the target genes, KEGG pathway analysis was conducted using the DAVID database (FDR< 0.05). According to the KEGG pathway analysis results, the target genes were mainly related to the MAPK signaling pathway, endocytosis, adherens junction, and SNARE interactions in vesicular transport (Fig. 2D).

    Fig. 2
    Bioinformatic analysis of predicted target genes of differentially expressed microRNAs from infection group of resistant line compared with susceptible line. (A) Biological process, (B) molecular function, (C) cellular component, (D) KEGG pathway.
    GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; FDR, false discovery rate.

    Additionally, we analyzed the interaction between 53 DE miRNAs and their target genes related to the immune response pathway and/or AIV by building a gene network using the Cytoscape software. A total of 238 interactions were identified between 53 DE miRNAs and 153 target genes, which were not overlapped (Fig. 3). The results of our network analysis suggested that these 53 DE miRNAs may target genes associated with the influenza A pathway, the transforming growth factor (TGF)-beta signaling pathway, the MAPK signaling pathway, and the TLR signaling pathway (Fig. 3).

    Fig. 3
    Network interaction between DE miRNAs and target genes related to immune response pathways. DE miRNAs were identified by comparing the infection samples of the resistant line and those of the susceptible line. The colors indicate the main pathways involved in the immune system and/or avian influenza virus.
    DE, differentially expressed; miRNA, microRNA; TGF, transforming growth factor; MAPK, mitogen-activated protein kinase.

    Validation of DE miRNAs and predicted target gene expression

    qRT-PCR was conducted to validate the results of the small RNA-seq of the 3 dpi samples. Specifically, we validated four miRNAs that were selected based on the read counts, their function in the immune system, and the number of target genes related to the immune response in each comparison (Fig. 4, Supplementary Table 6). Particularly, gga-miR-22-3p, gga-miR-146b-3p, gga-miR-27b-3p, and gga-miR-128-3p were upregulated in the infection samples compared with the control samples of the susceptible line (Fig. 4, Supplementary Table 6). In the resistant line, gga-miR-2188-5p, and gga-miR-451 were downregulated in the infection samples compared to the control (Fig. 4, Supplementary Table 6). In contrast, gga-miR-205a and gga-miR-203a were upregulated in the infected samples compared to the control (Fig. 4, Supplementary Table 6). Upon comparing the infection samples of the resistant and susceptible lines, gga-miR-21-3p, gga-miR-200a-3p, gga-miR-205a, and gga-miR-203a were found to be upregulated in the resistant line (Fig. 4, Supplementary Table 6). More importantly, the results of our qRT-PCR validation experiments were highly consistent with those of our small RNA-seq analyses (Fig. 4).

    Fig. 4
    Validation of DE miRNAs using qRT-PCR. (A) DE miRNAs in the infection group compared with the control group in the susceptible line. (B) DE miRNAs in the infection group compared with the control group in the resistant line. (C) DE miRNAs in the infection samples in the resistant line compared with the susceptible line. The expression level of miRNAs was normalized to the expression of the U1A gene. The error bars indicate the standard error of the mean. qRT-PCR was performed using pooled samples in triplicate. Some RNA samples did not pass the quality control criteria. In these cases, other samples from different batches were used for the validation of sequencing results (SD3C: S2D3C, S3D3C, S5D3C; SD3I: S1D3I, S2D3I, S3D3I, S5D3I; RD3C: R1D3C, R2D3C, R4D3C; RD3I: R1D3I, R2D3I, R3D3I, R5D3I).
    DE, differentially expressed; miRNA, microRNA; qRT-PCR, quantitative real-time polymerase chain reaction.

    *p < 0.05; **p < 0.01; ***p < 0.001.

    Furthermore, we also analyzed the expression levels of the target genes of the validated DE miRNAs. TRAF3 was identified as the target gene of gga-miR-22-3p and its expression level was downregulated in the infection samples compared with the control samples in the susceptible line (Supplementary Table 6, Fig. 5A). The expression level of NFATC3, which was expected to be regulated by gga-miR-27b-3p, was also downregulated in the infection samples compared to the control samples in the susceptible line (Supplementary Table 6, Fig. 5A). PI3KR1 was one of the target genes associated with gga-miR-205a and PLK2 was expected to be the target gene of gga-miR-2188-5p (Supplementary Table 6). In the resistant line, PI3KR1 was downregulated and PLK2 was upregulated in the infection samples compared to the control (Fig. 5B). Moreover, MAP3K20 and MEF2C were downregulated in the infection group of the resistant line compared to the susceptible line (Fig. 5C). MAP3K20 and MEF2C were predicted to be the target genes of gga-miR-200a-3p and gga-miR-203a, respectively.

    Fig. 5
    qRT-PCR results of predicted target genes of differentially expressed microRNAs. (A) gga-miR-22-3p target expression of TRAF3 and gga-miR-27b-3p target expression of NFATC3 in susceptible control and infection samples at 3 dpi. (B) gga-miR-205a target expression of PIK3R1 and gga-miR-2188-5p target expression of PLK2 in resistant control and infection samples at 3 dpi. (C) gga-miR-200a-3p target expression of MAP3K20 and gga-miR-203a target expression of MEF2C in infection samples of resistant line compared with the susceptible line at 3 dpi. The expression level of genes was normalized to the expression of GAPDH. The error bars represent the standard error of the mean. qRT-PCR was performed using pooled samples in triplicate.
    qRT-PCR, quantitative real-time polymerase chain reaction; dpi, day post-infection.

    **p < 0.01; ***p < 0.001.

    DISCUSSION

    In this study, small RNA-seq analyses were conducted to characterize the expression of miRNAs in two distinct H5N1-infected Ri chicken lines (HPAIV-resistant and -susceptible) to identify the mechanisms through which AIV affects the expression of miRNAs associated with the modulation of the immune response during AIV infection. Ri chickens (i.e., the most commonly grown indigenous chickens in Vietnam farms) were classified as susceptible or resistant to AIV via genotyping of the Mx and BF2 genes, respectively [23, 24]. miRNA can regulate the innate and adaptive immune response by modulating the production/release of cytokines and chemokines and controlling the development/differentiation of adaptive immune cells such as B and T cells [9]. Due to the role of miRNA in immune regulation, miRNA expression patterns have been analyzed for various viruses that affect chickens over the past decade, including AIV and Newcastle disease virus [11, 25]. To identify the mechanisms through which AIV influences the expression of miRNA and the role of miRNAs in host immune responses, small RNA-seq analyses were conducted to identify DE miRNAs in H5N1-infected chickens.

    Our analysis of the interaction network between DE miRNAs and immune-related target genes revealed that DE miRNAs affect gene pathways involved in AIV infection such as the influenza A pathway, as well as the TGF-beta, MAPK, and TLR signaling pathways. These findings suggest that these DE miRNAs play an important role in the immune response against AIV. Several studies have demonstrated that the TGF-beta, MAPK, and TLR signaling pathways play a vital role in influenza A virus infection [26, 27, 28]. The exogenous TGF-beta delayed the mortality of H5N1-infected mice and depleted viral titers via the neutralization of TGF-beta during H5N1 infection [26]. Influenza A virus infection activates the Raf, MEK, ERK, and p38 genes, all of which are key components of the MAPK signaling pathway [27]. MAPK modulates the inflammatory responses and T-cell response against the influenza A virus [27]. TLR ligands play a critical role in the initiation and modulation of antiviral responses and can reduce the shedding of AIV in the trachea [28]. Our results demonstrated that the DE miRNAs between the infection samples of the resistant and susceptible lines may regulate the H5N1 virus infection by controlling the expression of target genes associated with the TGF-beta, MAPK, and TLR signaling pathways.

    Furthermore, our findings indicated that gga-miR-146b-3p was upregulated in the infection samples compared to the control samples of the susceptible line. Specifically, gga-miR-146b-3p regulates autophagy and apoptosis by inhibiting the expression of AKT serine/threonine kinase 1 [29]. The AIV NS1 protein activates PI3K to prevent apoptosis for virus replication [30].

    gga-miR-27b-3p was upregulated in the infection samples compared with the control samples in the susceptible line. gga-miR-27b-3p was also upregulated in infectious bursal disease virus-infected DF-1 cells, which inhibited the replication of infectious bursal disease virus and improved the expression of type I interferons such as IRF3 and IFN-β by targeting SOCS3 and SCOS6 expression [31].

    In the resistant line, gga-miR-451 was downregulated in the infection samples compared with the control. gga-miR-451 was upregulated in lung tissues and DF-1 cells of Mycoplasma gallisepticum-infected chickens [32]. Moreover, gga-miR-451 could decrease the secretion of inflammatory cytokines and promote apoptosis to ensure efficient M. gallisepticum replication by repressing the expression of YWHAZ [32]. These results indicated that the downregulation of gga-miR-451 caused by AIV infection promoted inflammatory cytokine secretion and apoptosis in the resistant line.

    Furthermore, gga-miR-200a-3p was upregulated in the infection samples of the resistant line compared to the susceptible line. gga-miR-200a-3p was also upregulated in the intestinal mucosal layer of chickens during necrotic enteritis infection [33]. In this study, gga-miR-200a-3p was found to suppress the expression of TGFβ2, MAP2K4, and ZAK, which are involved in the MAPK signaling pathway [33].

    gga-miR-429-3p exhibited the highest number of interactions with target genes involved in influenza A virus-related pathways. gga-miR-429-3p can affect the TGF-beta pathway by regulating the expression level of TGFβ1 [34].

    This study also analyzed the expression levels of predicted target genes of DE miRNAs between infection samples from the susceptible and resistant lines. We predicted that gga-miR-22-3p and gga-miR-27b-3p may target TRAF3 and NFATC3, respectively. Furthermore, gga-miR-22-3p and gga-miR-27b-3p were upregulated in the infection samples compared with the control samples of the susceptible line, which coincided with a downregulation of the TRAF3 and NFATC3 target genes. TRAF3 may affect the replication of the influenza A virus and prevent its immune evasion by promoting the production of type I IFNs [35]. The NFAT family could regulate the production of immunomodulatory cytokines such as IL-2, IL-4, IFN-γ, and TNF-α in various immune cells [36].

    gga-miR-205a was upregulated in the infection samples compared to the control samples in the resistant line and this coincided with a downregulation of PIK3R1, which was the predicted target gene. Depending on the viral strain, the influenza A virus can affect the PI3K signaling pathway, which is involved in viral entry. Particularly, the PI3K/AKT signaling pathway could facilitate the internalization of the influenza A virus [37].

    gga-miR-2188-5p was downregulated in the infection samples of the resistant line and its target gene PLK2 was upregulated. PLK2 plays a critical role in the cell cycle, inflammation, and antiviral immunity [38].

    Our findings also revealed that gga-miR-200a-3p and gga-miR-203a were upregulated in the infection samples of the resistant line compared with the susceptible line. However, their target genes, MAP3K20 and MEF2C, were downregulated. Both the MAP3K20 and MEF2C genes are involved in the MAPK signaling pathway. In a previous study, we demonstrated that gga-miR-200a-3p could repress ZAK, MAP2K4, and TGFβ2 expression using a luciferase assay [33]. MP3K20 is a known activator of ERK, JNK, and p38, which are major MAPK pathways [39]. Moreover, MEF2C activity is directly related to the stimulation of B cell receptors, which are involved in the adaptive immune system, and its activation regulates B cell proliferation via the p38 MAPK cascade [40]. In this study, our findings demonstrated that the predicted target genes had a negative correlation with the identified DE miRNAs, and these genes were involved in various immune responses such as apoptosis, regulation of cytokine expression, viral replication, and proliferation of immune cells.

    Several studies identified miRNA expression on H5N1 infection [11, 12]. When comparing miRNA expression pattern with previous studies only few miRNAs showed similar pattern with our study. Gga-miR-7b was up-regulated in lung, spleen, thymus, and bursa of Fabricius from H5N1-infected chickens as up-regulated in RD3I samples compared with SD3I samples [11, 12]. Few miRNAs such as gga-miR-216b, gga-miR-29b-3p, and miR-7 were also down-regulated in infection group compared with control group in lung [12], but were not significantly expressed in our experiment [10]. Furthermore, target genes of down-regulated miRNAs in lung were also enriched in MAPK signaling pathway as KEGG pathway analysis result in this study [12]. Taken together, miRNA expression pattern may depend on the time for virus infection and tissues.

    In summary, the current study aimed to characterize the expression and potential role of miRNAs in the tracheal tissues of two distinct genetic lines (HPAIV-resistant and HPAIV-susceptible) of H5N1-infected Ri chickens by conducting small RNA-seq analysis. A total of 152 and 46 DE miRNAs were identified in the infection samples compared to the control samples in the susceptible and resistant lines, respectively. In the infection group, 53 DE miRNAs were identified in the resistant chicken line compared to the susceptible chicken line. The results of our bioinformatic analyses revealed that most of the DE miRNAs in the infection samples of the susceptible line relative to the resistant line may modulate the MAPK signaling pathway. Moreover, network analyses between DE miRNAs and target genes elucidated interactions involved in various pathways related to AIV infection. Collectively, our findings provide novel insights into the role of miRNAs in the immune response against HPAIV H5N1 infection in chickens.

    SUPPLEMENTARY MATERIALS

    Supplementary Table 1

    Number of Ri chickens used for avian influenza viruses challenge

    Click here to view.(26K, xls)

    Supplementary Table 2

    Read quality check of the control and H5N1-infected trachea samples in Ri chickens

    Click here to view.(29K, xls)

    Supplementary Table 3

    DE miRNAs in the H5N1-infected Ri chickens

    Click here to view.(67K, xls)

    Supplementary Table 4

    Relationship between miRNA and mRNA sequencing of H5N1-infected trachea tissues at 3 day post-infection (resistant line vs. susceptible line)

    Click here to view.(35K, xls)

    Supplementary Table 5

    List of all target genes of DE miRNAs from comparisons between infection samples in the resistant line compared with the susceptible line

    Click here to view.(70K, xls)

    Supplementary Table 6

    List of validated differentially expressed miRNAs and immune-related target genes

    Click here to view.(32K, xls)

    Supplementary Fig. 1

    Sequencing chromatogram of the Mx gene of Ri chickens.

    Click here to view.(3M, ppt)

    Notes

    Funding:This research was funded by the National Research Foundation grant (NRF-2021R1A2C2005236) of the Republic of Korea.

    Conflict of Interest:The authors declare no conflicts of interest.

    Author Contributions:

    • Conceptualization: Kang S, Lillehoj HS, Hong YH.

    • Data curation: Kang S, Hong YH.

    • Formal analysis: Kang S.

    • Funding acquisition: Hong YH.

    • Investigation: Kang S, Heo J, Vu TH, Kim C, Lillehoj HS, Hong YH.

    • Methodology: Kang S, Heo J, Vu TH, Kim C, Hong YH.

    • Project administration: Hong YH.

    • Resources: Hong YH.

    • Software: Hong YH.

    • Supervision: Hong YH.

    • Validation: Kang S, Hong YH.

    • Visualization: Kang S, Hong YH.

    • Writing - original draft: Kang S.

    • Writing - review & editing: Kang S, Lillehoj HS, Hong YH.

    ACKNOWLEDGEMENTS

    The authors thank the Department of Biochemistry and Immunology Laboratory of the National Institute of Veterinary Research, Vietnam, for conducting the animal experiments described in this study.

    References

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    Publication Types
    Original Article
    MeSH Terms
    Animals
    Breeding
    Chickens*
    Encyclopedia
    Gene Expression
    Gene Ontology
    Genome
    High-Throughput Nucleotide Sequencing
    Humans
    Immune System
    Immunity
    Influenza A virus
    Influenza A Virus, H5N1 Subtype*
    Influenza in Birds*
    MicroRNAs
    MicroRNAs*
    Mitogen-Activated Protein Kinases
    Poultry
    Real-Time Polymerase Chain Reaction
    Sequence Analysis, RNA
    Signal Transduction
    Toll-Like Receptors
    Trachea*
    Metrics
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