
==== Front
Poult Sci
Poult Sci
Poultry Science
0032-5791
1525-3171
Elsevier

S0032-5791(24)00704-1
10.1016/j.psj.2024.104125
104125
IMMUNOLOGY, HEALTH AND DISEASE
Effects of H9N2 avian influenza virus infection on metabolite content and gene expression in chick DF1 cells
Zhang Yijia *
Li Li *
Xin Xin *
Chang Lifeng *
Luo Haowei *
Qiao Wenna *
Xia Jun †
Ping Jihui ‡
Su Juan sujuan@njau.edu.cn
⁎1
⁎ Laboratory of Animal Neurobiology, College of Veterinary Medicine, Nanjing Agricultural University, Nanjing, 210095, China
† Institute of Veterinary Medicine, Xinjiang Academy of Animal Science, Key Laboratory for Prevention and Control of Herbivorous Animal Diseases of the Ministry of Agriculture and Rural Affairs & Xinjiang Animal Disease Research Key Laboratory, Urumchi, 830000, China
‡ MOE Joint International Research Laboratory of Animal Health and Food Safety, Engineering Laboratory of Animal Immunity of Jiangsu Province, College of Veterinary Medicine, Nanjing Agricultural University, Nanjing, 210095, China
1 Corresponding author: sujuan@njau.edu.cn
30 7 2024
10 2024
30 7 2024
103 10 10412526 2 2024
24 7 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
After viral infection, the virus relies on the host cell's complex metabolic and biosynthetic machinery for replication. However, the impact of avian influenza virus (AIV) on metabolites and gene expression in poultry cells remains unclear. To investigate this, we infected chicken embryo fibroblasts DF1 cells with H9N2 AIV at an MOI of 3. Our aim was to explore how H9N2 AIV alters DF1 cells metabolic pathways to facilitate its replication. We employed metabolomics and transcriptomics techniques to analyze changes in metabolite content and gene expression. Metabolomics analysis revealed a significant increase in glutathione-related metabolites, including reduced glutathione (GSH), oxidized glutathione (GSSG) and total glutathione (T-GSH) upon H9N2 AIV infection in DF1 cells. Elisa results confirmed elevated levels of GSH, GSSG, and T-GSH consistent with metabolomics findings, noting a pronounced increase in GSSG compared to GSH. Transcriptomics showed significant alterations in genes involved in glutathione synthesis and metabolism post-H9N2 infection. However, adding the glutathione synthesis inhibitor BSO exogenously significantly promoted H9N2 replication in DF1 cells. This was accompanied by increased mRNA levels of pro-inflammatory cytokines (IL-1β, IFN-γ) and decreased mRNA levels of anti-inflammatory cytokines (TGF-β, IL-13). BSO also reduced catalase (CAT) gene expression and inhibited its activity, leading to higher reactive oxygen species (ROS) and malondialdehyde (MDA) level in DF1 cells. qPCR results indicated decreased mRNA levels of Nrf2, NQO1, and HO-1 with BSO, ultimately increasing oxidative stress in DF1 cells. Therefore, the above results indicated that H9N2 AIV infection in DF1 cells activated the glutathione metabolic pathway to enhance the cell's self-defense mechanism against H9N2 replication. However, when GSH synthesis is inhibited within the cells, it leads to an elevated oxidative stress level, thereby promoting H9N2 replication within the cells through Nrf2/HO-1 pathway. This study provides a theoretical basis for future rational utilization of the glutathione metabolic pathway to prevent viral replication.

Key words

H9N2
GSH
glutathione metabolism
oxidative stress
DF1 cell
==== Body
pmcINTRODUCTION

The Influenza viruses, which are part of the Orthomyxoviridae family and the influenza virus genus, are a type of RNA virus with a segmented, single-stranded, negative sense envelope (Cargnin Faccin and Perez, 2024). Influenza viruses can be classified into 4 categories: A (IAV), B (IBV), C (ICV), and D (IDV). IAV, one of them, is a pathogen of great clinical significance, presenting a grave danger to both the poultry industry and public health. As a low pathogenic avian influenza virus (AIV), H9N2 AIV is one of the main subtypes of avian influenza virus, widely distributed in many parts of the world (Sikht et al., 2022). The prevalence of H9N2 avian influenza virus in poultry is extensive, and when combined with other pathogens, it can result in significant mortality rates and substantial economic losses for the breeding industry (Dong et al., 2022). At present, inactivated whole virus vaccines are still the main means of preventing and controlling H9N2 AIV in China. Although vaccination can alleviate clinical symptoms and reduce economic losses, immunized chickens can still be re-infected with H9N2 AIV, and viral shedding can be detected from oropharyngeal and cloacal swabs (Sun et al., 2024). Hence, it is highly important to create vaccines that can stimulate a broad spectrum of response and provide enduring protection against H9N2 AIV.

Metabolomics can identify and quantify all metabolites in organisms. Metabolites are the final products regulated by genes and proteins, therefore closely related to phenotype (Zhang et al., 2020). Influenza viruses must rely on the "resources" in the host cells to ensure their replication, which inevitably leads to changes in the content of metabolites and gene expression in the cell during this process. Therefore, analyzing the altered metabolic pathways is crucial for elucidating new target molecules related to viral infection and seeking new prevention and treatment methods (Noto et al., 2014; Chen et al., 2024). The application of metabolomics has contributed to the comprehension of the replication and pathogenesis of influenza viruses, facilitating extensive investigation into the distinct metabolites that occur following cellular infection with influenza viruses. Viral infection can often cause alterations in metabolic pathways like glycolysis, lipid production, the tricarboxylic acid cycle, and nucleotide creation (Wang et al., 2023). Following H1N1 (WSN) infection in A549 cell, changes in metabolites associated with the tricarboxylic acid (TCA) cycle were observed, primarily influencing purine, lipid, and glutathione metabolism (Tian et al., 2019). The variations in metabolic pathways can differ depending on the particular virus and the kinds of host cells that are infected. Currently, metabolomics is commonly used to study the changes in metabolite content caused by influenza virus infection in mammalian cells, with relatively little research on avian cells.

The use of transcriptome technology has become crucial in investigating the cellular signaling mechanism. It enables the detection of alterations in the expression profile of host genes during viral infection (Cao et al., 2019). Different gene expression patterns were induced in normal human bronchial epithelial cells through transcriptome sequencing, revealing the infection of highly virulent H7N9 and pandemic H1N1, respectively (Hsieh et al., 2022). Limited information about the biological processes of diseases can be obtained from single-omics, whereas multi-omics combines data from various omics levels to achieve a more comprehensive comprehension of disease onset and progression. The combination of metabolomics and transcriptomics help to clarify the mechanism of disease occurrence and reveal biomarkers and target genes. To identify alterations in the metabolic profile of neurons during the initial phase of Japanese encephalitis virus (JEV) infection, a combined analysis of metabolomics and transcriptome was employed. Li previously discovered that there was an elevation in the flow of glycolysis and its branched-chain pentose phosphate pathway (PPP), while the utilization of glucose in oxidative phosphorylation (OXPHOS) was hindered (Li et al., 2021). The overall changes in key metabolite content and gene expression caused by H9N2 AIV infection in DF1 cells are still unknown.

This study aims to investigate the effects of H9N2 AIV infection on metabolic pathways and gene expression in chicken embryo fibroblasts DF1 cells, and aiming to elucidate the mechanism by which H9N2 alters DF1 cells metabolic pathways to facilitate its own replication. The analysis revealed that viral infection resulted in alterations in the composition of numerous metabolites and the expression of genes in DF1 cells. Based on the significant changes in glutathione content in metabolites, the impact and related mechanisms of changes in glutathione content in poultry cells on influenza virus replication were preliminarily explored.

MATERIALS AND METHODS

Cell and Viruses

DF1 cells are a cell line derived from chicken embryo fibroblasts, widely used in biomedical research. Due to the absence of endogenous viruses, DF1 cells are commonly used to study the infection mechanisms of avian viruses and for vaccine development (Huang et al., 2017; Briggs et al., 2024). DF1 cells were incubated in Dulbecco's modified Eagle's medium (DMEM, Gibco), containing 10% fetal bovine serum (FBS, Gibco), at a temperature of 37°C in a 5% CO2 environment. Anhui BRI-99/2017 (H9N2) chicken were cultured in specific pathogen-free (SPF) chicken embryos at 37°C for 48 h. A plaque assay was used to determine viral titers. The viruses were kept at a temperature of -80°C until they were used. The experiments were repeated in triplicate.

Cell Sample Preparation

DF1 cells at 95% confluence were exposed to H9N2 AIV at a multiplicity of infection (MOI) of 3 for 1 h in serum-deprived DMEM. Following a 12-h infection with H9N2 AIV, the cells were rinsed 3 times using PBS and then scraped. The cells that were gathered were promptly frozen in liquid nitrogen and kept at a temperature of -80°C.

Metabolomics Analysis

Following the gradual thawing of the samples at a temperature of 4°C, each sample was thoroughly mixed with 300 μL of prechilled 80% methanol using a vortex. After rapid freezing, application of ultrasound, and subsequent centrifugation of the specimen, the resulting liquid was subjected to freeze-drying and subsequently reconstituted using a 10% methanol solution. Ultimately, the remedy was introduced into the LC-MS/MS system for analysis. To assess the system's stability and the reliability of the experimental data, quality control samples were prepared by combining equal volumes of each sample. Each assay used a repeat of 3 wells.

The raw data files were processed using Compound Discoverer 3.1 (CD3.1, Thermo Fisher Scientific, Waltham, MA) to peak alignment, peak picking, and quantitation for each metabolite. Metabolite identification utilized various databases including mzCloud, mzVault, and Masslist. To examine the distribution and separation trend of the 2 groups of samples, a principal component analysis was conducted. Differential metabolites were identified as those that satisfied the criteria of variable importance in the projection (VIP > 1) and a p-value of Student's t-test  <  0.05. The identified metabolites were classified and annotated by referring to the KEGG databases.

Transcriptome Sequencing

The Trizol method was used to extract total RNA from DF1 cells, following the manufacturer's protocol. RNA quality was assessed by agarose gel electrophoresis, Nanodrop micro spectrophotometer and Agilent 2100. Following the enrichment of eukaryotic mRNA using Oligo (dT) beads, the mRNA was fragmented into shorter fragments and subsequently converted into cDNA through reverse transcription. After repairing the purified double-stranded cDNA fragments, a base was added and they were then ligated to Illumina sequencing adapters. Purification of the ligation reaction was performed using the AMPure XP Beads at a concentration of 1.0X. PCR was used to amplify the polymerase chain reaction. Gene Denovo Biotechnology Co. (Guangzhou, China) sequenced the cDNA library using Illumina Novaseq6000. Each assay used a repeat of 3 wells. In order to obtain high-quality clean reads, the reads obtained from the sequencing machines were further filtered using fastp. Differential genes were identified as genes that satisfy the criteria of |Fold Change| ≥  2 and a false discovery rate (FDR) <  0.05. Gene Ontology annotation analysis and KEGG functional enrichment analysis were performed on significantly differential genes. The Gene Ontology consists of 3 ontologies: molecular function, cellular component, and biological process.

Integrated Analysis of Transcriptomics and Metabolomics

The differentially expressed genes and metabolites were gathered and summarized for analysis of the KEGG pathway network.

Real-time RT-qPCR

Total RNA was extracted using the TRIZOL reagent (Vazyme, China). For quantitative real-time PCR (qPCR), the HiScript II 1st-strand cDNA synthesis kit (Vazyme, China) was used to synthesize cDNA from total RNA. Real-time qPCR was performed using AceQ qPCR SYBR Green Master Mix (Vazyme, China) in a Roche LightCycler 96. The amplification procedure was carried out in the following manner: initial denaturation at 95°C for 5 min, followed by 40 cycles of denaturation at 95°C for 10 s and annealing at 60°C for 30 s. The relative levels of the targeted genes were determined using the 2−ΔΔCt method. The experiments were repeated in triplicate. Table 1 displays the primers utilized for identifying potential genes. The internal reference genes utilized were GAPDH.Table 1 Sequences of target genes primers.

Table 1Genes1	Forward	Reverse	
GPX8	5′-CACAGGGAGTTTGGTCCCTC-3′	5′-GCTCTGCTTCTGATCCCAGG-3′	
IDH1	5′-CTCTGTTGCACAAGGTTATGGC-3′	5′-AATGGGGTTCGTGGAGGTTT-3′	
GGT5	5′-TTTGATCTGCACGTCGGTGA-3′	5′-TCACGGGCATTGATCACCTC-3′	
GSS	5′-CCCCGAGAGAACCTCCTACA-3′	5′-CCCTGCCTGACATAGACACC-3′	
ANPEP	5′-TGGTTGAATGAGGGCTTTGC-3′	5′-TGCTGGGGTGTTGATCTCAT-3′	
CHAC1	5′-CTGGTGGACTTCATGCGCTA-3′	5′-TCCTCCAGGGCTTTCTCTGA-3′	
TNF-α	5′-ATCCTCACCCCTACCCTGTC-3′	5′-AACTCATCTGAACTGGGCGG-3′	
IL-1β	5′-GCCTGCAGAAGAAGCCTCG-3′	5′-GGAAGGTGACGGGCTCAAAA-3′	
IFN-γ	5′-AACAACCTTCCTGATGGCGT-3′	5′-TGAAGAGTTCATTCGCGGCT-3′	
IL-13	5′-AATGACACCAGAGTGGCACA-3′	5′-AGTCGGTCATGTTGTCCAGC-3′	
TGF-β	5′-GGATCCACGAACCCAAAGGT-3′	5′-TCCGGCCCACGTAGTAAATG-3′	
Nrf2	5′-GGGCAAGGCGTGAAGTTTTT-3′	5′-GGCTTTCTCCCGCTCTTTCT-3′	
NQO1	5′-AACCTCTTTCAACCACGCCA-3′	5′-AAGCACTCGGGGTTCTTGAG-3′	
HO-1	5′-CCACGAGTTCAAGCTGGTCA-3′	5′-AGCCTCAGGACATGGGATCT-3′	
CAT	5′-TGGCGCCCCGAACTATTATC-3′	5′-TGAAACGCTGCACATCTCCT-3′	
GAPDH	5′-GGGCACGCCATCACTATCTT-3′	5′-TCACAAACATGGGGGCATCA-3′	
1 Gene names are indicated in italics in the manuscript.

Cell Viability Assay

DF1 cells were cultured in 96-well dishes and subsequently exposed to Buthionine Sulfoximine (BSO), an inhibitor of glutamylcysteine synthetase, at concentrations of 2, 5, 7, and 10 μM. After 48 h, the cells were rinsed thrice using PBS. In each well, 10 microliters of CCK8 and 90 microliters of fresh medium were added, followed by incubation at 37°C for 1 h. The absorbance was then measured at 450 nanometers using a microplate reader from TECAN in Switzerland. The experiments were repeated in triplicate.

BSO Treatment of DF1 Cells

DF1 cells were treated continuously with 7 μM glutamylcysteine synthetase inhibitor BSO (Sigma) from 24 h before through the 12 and 24 h after MOI of 1 H9N2 AIV infection. Collect supernatant or cell samples from each group, and perform 3 independent replicates.

Plaque Assays

The infectious titers of the supernatant samples were determined by plaque assay. The liquid samples were diluted in DMEM by a factor of 10 and then applied to MDCK cells monolayers. The MDCK cell was rinsed 2 times using 1 × PBS and subsequently exposed to 200 μL of various virus dilutions. Following a 1-h incubation at a temperature of 37°C, the cells were covered with DMEM that consisted of 1.8% Sea Plaque agarose (Lonza) and 1 μg/mL TPCK-trypsin, and then incubated at 37°C. Plaque formation was observed after 2 d infection. The experiments were repeated in triplicate.

Antioxidant Parameters Measurements

The cells were rinsed with PBS and subsequently incubated in NP40 lysis buffer (Beyotime, Shanghai, China) supplemented with 1 mM phenylmethanesulfonyl fluoride (PMSF, Beyotime, Shanghai, China) for 20 min. The levels of superoxide dismutase (SOD), catalase (CAT), reduced glutathione (GSH), total glutathione (T-GSH), and oxidized glutathione (GSSG) were measured using the appropriate detection kits according to the instructions provided by the manufacturer. The reactive oxygen species (ROS) and malondialdehyde (MDA) contents were detected by a fluorescent probe DCFH-DA (Beyotime, Shanghai, China) and thiobarbituric acid assay (Beyotime, Shanghai, China), respectively. The protein concentration in cells were measured using a BCA assay kit as per the instructions provided by the manufacturers. The experiments were repeated in triplicate.

Statistical Analysis

Statistical analysis of the data was performed using GraphPad Prism Version 8.0 software. Student's t-tests was used to compare the notable variations among groups. Statistically significant differences between the various groups were determined using a significance level of p ≤ 0.05

RESULTS

Metabolomic Changes Associated With H9N2 AIV Infection

To identify the metabolites responsible for the responses of DF1 cells to H9N2 AIV infection, metabolomic changes were analyzed using LC-MS/MS techniques. Principle component analysis (PCA, Supplementary Figure S1A) and supervised orthogonal projections to latent structures-discriminant analysis (OPLS-DA, Supplementary Figure S1B) indicated a clear distinction in distribution between the H9N2 AIV-infected group and the mock-infected group. The OPLS-DA model's variable importance in projection (VIP) and univariate analysis's p value were utilized to further filter distinct metabolites. Differential metabolites were identified based on a combination of VIP values (> 1) and p-value (< 0.05) obtained from Student's t-tests. In positive ion mode, 120 metabolites showed significant differences, with 48 significantly upregulated and 72 significantly downregulated metabolites between the H9N2 AIV-infected and mock-infected group (Figure 1A). In negative ion mode, 120 metabolites exhibited significant variations between the H9N2 AIV-infected group and the mock-infected group, including 19 metabolites that were significantly increased and 46 metabolites that were significantly decreased (Figure 1B). Volcano plots were used to visualize differential metabolites in different ion modes. Heat maps were generated to cluster the abundance patterns of these metabolites, highlighting notable variations across different ion modes (Figures 1C-1D).Figure 1 Analysis of differential metabolites and KEGG pathway enrichment in DF1 cells infected with H9N2 AIV (n = 3). (A) Volcano plot of differential metabolites in positive ion mode. (B) Volcano plot of differential metabolites in positive ion mode negative ion mode. The red, blue and grey circles represent the upregulated, downregulated and no differential metabolites. (C) Heatmap of differential metabolites in positive ion mode. Each row is a differential metabolite, and each column represents a replicate of a group. (D) Heatmap of differential metabolites in negative ion mode. (E) KEGG pathway enrichment analysis (the most enriched top 20 pathway terms). The ordinate represents the KEGG signal pathway, while the abscissa represents the enrichment factor. The size of the dots represents gene ratio and the color of the dots represents enrichment significance. Boxplot of the (F) T-GSH, (G) GSH, (H) GSSG. Significance levels: *, p ≤  0.05; **, p ≤  0.01; ***, p ≤  0.001; ns, not statistically significant.

Figure 1

The pathways related to metabolite alterations were identified using the KEGG Metabolome Database. A bubble plot (Figure 1E) displayed the outcomes of KEGG enrichment analysis. The suggested pathway impact values indicated that primary enriched metabolic pathways of distinct metabolites during H9N2 avian influenza virus infection included the Sphingolipid signaling pathway, cysteine and methionine metabolism, synthesis of thyroid hormone, and glutathione metabolism, among others. These metabolic pathways exhibited an abundance of glutathione metabolites. According to the data presented in Figures 1F-1H, the H9N2 AIV-infected group showed a significant increase in levels of T-GSH, GSH, and GSSG compared to the mock-infected group. The results suggested that H9N2 AIV infection alters the metabolite content of the glutathione metabolism pathway in DF1 cells.

Alterations in Transcriptome Induced by H9N2 AIV Infection

The transcriptional alterations in response to H9N2 avian influenza virus infection were determined. Using criteria of |log2FC| > 1 and FDR <  0.05, the genes that underwent significant alterations were identified. According to the data presented in Figure 2A, a total of 3921 genes showed significant changes. Among these, 3313 genes were up-regulated, while 608 genes were down-regulated in the H9N2 AIV-infected group compared to the mock-infected group. Enriched genes that underwent significant alterations were further analyzed using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. For GO enrichment analysis, the functions of differentially expressed genes were categorized into 3 groups: biological process, cell composition, and molecular function (Figure 2B). Biological process included cellular process, biological regulation, and metabolic process, among others. Cellular components encompassed cells, cell parts, and membranes. Molecular functions included binding, catalytic activity, and molecular transducer activity.Figure 2 Transcriptomics analysis in H9N2 AIV-infected and mock-infected DF1 cells (n = 3). (A) Volcano plot of significantly altered genes. (B) GO analysis of significantly altered genes. (C) KEGG pathway enrichment analysis of significantly altered genes (the most enriched top 20 pathway terms). The ordinate represents the KEGG signal pathway, while the abscissa represents the enrichment factor. The size of the dots represents gene ratio and the color of the dots represents enrichment significance. (D) significantly altered genes (GSS, IDH1, RRM2B, GPX8, GSTT1, GGT1, ODC1, ANPEP, GGT5, CHAC1, and GSTA3) expression in the glutathione metabolism pathway of H9N2 AIV-infected or mock-infected cells. The fold change of (E) GSS, (F) IDH1, (G) GPX8, (H) ANPEP, (I) CHAC1, (J) GGT1, (K) GGT5 in the glutathione pathway. Significance levels: *, p  ≤  0.05; **, p  ≤  0.01; ***, p  ≤  0.001; ns, not statistically significant.

Figure 2

To further understand the function of significantly altered genes in signaling pathways, the enriched genes were analyzed using the KEGG database to identify significantly abundant pathways (Figure 2C). Among the top 20 KEGG pathways were neuroactive ligand-receptor interaction, cytokine-cytokine receptor interaction, and ECM-receptor interaction, among others. Additionally, glutathione metabolism was prominently enriched in the pathways affected by the H9N2 virus infection. In the glutathione metabolism pathway, 11 genes were affected by H9N2 virus infection, including GSH synthetase (GSS), Isocitrate dehydrogenase 1 (IDH1), Ribonucleotide reductase regulatory TP53 inducible subunit M2B (RRM2B), Glutathione peroxidase 8 (GPX8), Glutathione S-transferase theta 1 (GSTT1), Gamma-glutamyltransferase 1 (GGT1), Ornithine decarboxylase 1 (ODC1), Aminopeptidase N (ANPEP), Gamma-glutamyltransferase 5 (GGT5), Glutathione-specific gamma-glutamylcyclotransferase 1 (CHAC1), Glutathione S-transferase alpha 3 (GSTA3) gene (Figures 2D -2K).

Joint Analysis of Transcriptomics and Metabolomics

In order to establish the relationship between transcriptomics and metabolomics data, we conducted a correlation network analysis of different metabolites and genes that were significantly altered, alongside KEGG pathway analysis. Three differential metabolites and 11 significantly altered genes were enriched into the glutathione metabolism pathway (Figure 3A). Among them, the GSS gene is involved in the synthesis of glutathione, the GGT, ANPEP, and CHAC1 genes are involved in the degradation process of glutathione, and the IDH1 and GPX8 genes are involved in the mutual transformation process between GSH and GSSG. Therefore, H9N2 virus infection of DF1 cells caused changes in the content of glutathione and in the expression of genes related to its synthesis, degradation, and transformation in the glutathione metabolism pathway. For specific values regarding the effects of H9N2 influenza virus infection on the relative abundance of glutathione metabolites and the expression of related genes in DF1 cells, please refer to Table 2.Figure 3 Glutathione metabolic pathway in metabolomics and transcriptomics joint analysis data (n = 3). (A) Glutathione synthesis, degradation, and transformation pathways including significantly differential expressed metabolites and genes. Circles represent metabolites, squares represent genes. For genes, red indicates upregulation and green indicates downregulation. For metabolites, red indicates upregulation and blue indicates downregulation.

Figure 3

Table 2 Effects of H9N2 AIV infection on glutathione metabolites and related gene expression in DF1 cells.

Table 2Items	Mock	H9N2	SEM	p-value	
Glutathione metabolites1					
  GSH	1725109	2067208*	116522	0.015	
  GSSG	134788	260321⁎⁎⁎	25918	0.001	
  T-GSH	511388	842751⁎⁎	90600	0.004	
Glutathione metabolism
related genes2,3					
  ANPEP (count)	1.00	57.67⁎⁎⁎	2.33	< 0.0001	
  GSS (count)	931.70	165.30⁎⁎⁎	16.49	< 0.0001	
  CHAC1 (count)	96.00	257.70⁎⁎	33.21	0.008	
  GGT1 (count)	3.67	73.33⁎⁎⁎	4.19	< 0.0001	
  GGT5 (count)	0.33	16.67⁎⁎⁎	0.94	< 0.0001	
  GPX8 (count)	90.33	14.33⁎⁎	13.40	0.005	
  IDH1 (count)	4872.00	787.00⁎⁎⁎	228.50	< 0.0001	
1 Metabolomics data were processed for peak identification, filtering, and alignment, yielding results including mass-to-charge ratio, retention time, and peak area. Quantitative results were normalized for comparison across different scales, resulting in identified and quantified data. Thus, the results of metabolomics are at the relative abundance level.

2 The unit used for transcriptome sequencing results is gene expression counts.

3 Gene names are indicated in italics in the manuscript.

*, **, ***Different superscript asterisks in each indicator represent a significant difference indicates (*, p  ≤  0.05; ⁎⁎, p  ≤  0.01; ⁎⁎⁎, p  ≤  0.001)

H9N2 AIV Disturbs Glutathione Metabolism in the DF1 Cells

To further investigate the effect of H9N2 AIV on glutathione metabolism in DF-1 cells, the levels of T-GSH, GSH, GSSG, the ration of GSH/GSSG, and the expression of glutathione metabolism-related genes were assayed using commercial assay kits and qPCR. As shown in Figure 4A and Figure 4C, the intracellular levels of T-GSH and GSSG in DF1 cells were significantly increased at 24 and 36 h after H9N2 AIV infection. Additionally, the intracellular levels of GSH in the DF1 cells was significantly enhanced at 24h after H9N2 AIV infection (Figure 4B). However, the balance of glutathione metabolism and redox status in DF1 cells was disrupted by H9N2 AIV infection, leading to a significant reduction in the GSH/GSSG ratio at 24 and 36 h after infection (Figure 4D). qPCR analysis indicated that the mRNA level of GPX8 (Figure 4E), IDH1 (Figure 4F) were downregulated, while the mRNA level of GSS (Figure 4G), GGT5 (Figure 4H), ANPEP (Figure 4I) and CHAC1 (Figure 4J) were significantly upregulated in DF1 cells infected with H9N2 AIV. These results intuitively suggested that H9N2 AIV infection induces disturbance in glutathione metabolism.Figure 4 The infection of DF1 cells with H9N2 AIV impacts the gene expression associated with the pathway of glutathione metabolism and increases the levels of intracellular GSH (n = 3). (A) Intracellular total GSH, (B) GSH, (C) GSSG levels, and (D) GSH/GSSG ratio were detected by commercial assay kits. The mRNA levels of (E) GPX8, (F) IDH1, (G) GGT5, (H) GSS, (I) ANPEP and (J) CHAC1 were assessed by qPCR. The results are presented as means ± SEM. Significance levels: *, p  ≤  0.05; **, p  ≤  0.01; ***, p  ≤  0.001; ns, not statistically significant.

Figure 4

Reduced Endogenous GSH Synthesis Promotes the Replication of H9N2 AIV in DF1 Cells

It is important to mention that the H9N2 AIV infection in DF-1 cells increased the intracellular concentration of glutathione. Therefore, we used the glutamylcysteine synthetase inhibitor BSO to examine the impact of reduced endogenous GSH levels on H9N2 AIV replication in cells. As shown in Figure 5A, no significant differences were observed in cell viability between cells treated with 7 μM or 10 μM BSO. The doses of 7 μM BSO was chosen for this experiment because slight changes in cell morphology were observed under the microscope. The endogenous GSH content was significantly decreased in DF1 cells treated with 7 μM BSO for 24 h (Figure 5B). We found that influenza virus titer significantly increased in DF1 cells treated with 7 μM BSO for 24 h before and 12 h after infection (Figure 5C). The results showed that reduced endogenous GSH synthesis in cells promoted H9N2 AIV replication.Figure 5 Glutamylcysteine synthetase inhibitor BSO treatment reduces intracellular GSH levels in DF1 cells and promotes H9N2 AIV replication (n = 3). (A)Viability of DF1 cells under treated with different doses of BSO (B) The DF1 cells were pretreated with/without BSO for 24  h. Intracellular total GSH were detected by commercial assay kits. (C) DF1 cells were pre-exposed to BSO for a duration of 24 h, followed by infection with or without H9N2 AIV. Samples of the liquid above were collected 12 h and 24 h after the infection. Plaque assay was used to determine the viral titers in the supernatants. The findings are displayed as averages plus standard error of the mean. Significance levels: *, p  ≤  0.05; **, p ≤  0.01; ***, p  ≤  0.001; ns, not statistically significant.

Figure 5

The Virus Infection After BSO Treatment Effects the Intracellular Glutathione Content and Glutathione Pathway Related Genes Expression

To further investigate the impact of virus infection on glutathione metabolism in DF-1 cells following BSO treatment, we measured the levels of T-GSH, GSH, GSSG, the GSH/GSSG ratio, and the expression of genes related to glutathione metabolism using commercial assay kits and qPCR. The levels of T-GSH (Figure 6A) and GSH (Figure 6B) were significantly reduced in DF1 cells treated with 7 μM BSO for 24 h before and 12 h after infection. However, the levels of GSSG (Figure 6C) and the GSH/GSSG ratio (Figure 6D) did not show significant changes among the different groups. Additionally, RT-qPCR analysis revealed that virus infection following BSO treatment significantly upregulated the GPX8 (Figure 6E) and CHAC1 mRNA level (Figure 6J), while downregulating GGT5 (Figure 6G), GSS (Figure 6H), and ANPEP mRNA levels (Figure 6I) in DF1 cells compared to the Flu group. These results indicated that the reduction in endogenous glutathione synthesis caused by BSO treatment further exacerbates differences in intracellular glutathione content and gene expression related to the glutathione metabolism pathway induced by viral infection.Figure 6 H9N2 AIV infection after BSO treatment affects the expression of glutathione metabolism pathway related genes and downregulates intracellular GSH levels. Prior to infection with or without H9N2 AIV for 12 h, DF1 cells were either pre-exposed to BSO for 24 h or left untreated (n = 3). Commercial assay kits were used to measure the levels of total (A) GSH, (B) GSH, (C) GSSG levels and the (D) GSH/GSSG ratio in DF1 cells. The mRNA levels of (E) GPX8, (F) IDH1, (G) GGT5, (H) GSS, (I) ANPEP and (J) CHAC1 were assessed by qPCR. The results are presented as means ± SEM. Significance levels: *, p ≤  0.05; **, p  ≤  0.01; ***, p  ≤  0.001; ns, not statistically significant.

Figure 6

H9N2 AIV Infection After BSO Treatment Increases the Pro-Inflammatory Cytokines Level and Weakens Antioxidant Capacity of DF1 Cells

Next, we examined the impact of H9N2 AIV infection on the expression of inflammatory factors in DF1 cells. qPCR results showed that H9N2 AIV infection significantly increased mRNA levels of pro-inflammatory cytokines TNF-α (Figure 7A), IL-1β (Figure 7B), IFN-γ (Figure 7C) and decreased mRNA levels of anti-inflammatory cytokines IL-13 (Figure 7D) and TGF-β (Figure 7E) in DF1 cells. Additionally, compared to the control group, the addition of the GSH synthesis inhibitor BSO significantly elevated the expression levels of pro-inflammatory cytokines TNF-α (Figure 7A), IL-1β (Figure 7B), IFN-γ (Figure 7C) while inhibiting the expression of anti-inflammatory cytokines IL-13 (Figure 7D). However, the addition of BSO did not lead to significant changes in the expression of the anti-inflammatory cytokine TGF-β compared to the control group (Figure 7E). Next, we assessed the impact of H9N2 AIV infection on the antioxidant capacity of DF1 cells. qPCR results revealed that influenza virus infection significantly increased CAT mRNA level (Figure 7G) and enhanced CAT enzyme activity (Figure 7K), along with elevating intracellular ROS levels (Figure 7M-7N). However, H9N2 AIV infection did not significantly affect SOD activity (Figure 7J) or MDA content (Figure 7L) compared to the control group. In contrast, the addition of BSO significantly reduced mRNA expression levels of CAT (Figure 7G) and inhibited CAT enzyme activity (Figure 7K) in DF1 cells compared to the control group. Conversely, BSO supplementation increased MDA content (Figure 7L) and ROS levels (Figure 7M-7N) in cells but did not significantly affect SOD enzyme activity (Figure 7J). Additionally, we observed that BSO supplementation reduced the expression levels of Nrf2 (Figure 7F), NQO1 (Figure 7H), and HO-1 (Figure 7I) genes closely associated with oxidative stress.Figure 7 After undergoing BSO treatment, the infection of H9N2 AIV leads to an elevation in the levels of pro-inflammatory cytokines and a decrease in the antioxidant capacity of cell (n = 3). Prior DF1 cells were pretreated with/without BSO for 24  h, then infected with or without H9N2 AIV for 12 h. qPCR was used to evaluate the mRNA expression of (A) TNF-α, (B) IL-1β, (C) IFN-γ, (D) IL-13, (E) TGF-β, (F) Nrf2, (G) CAT, (H) NQO1, and (I) HO-1 (J) SOD activity, (K) CAT activity and (L) MDA levels were detected by commercial assay kits, (M-N) ROS levels were assessed by fluorescence microscopy using DCFH-DA probe and Image-Pro Plus software. The results are presented as means ± SEM. Significance levels: *, p  ≤  0.05; **, p  ≤  0.01; ***, p  ≤  0.001; ns, not statistically significant.

Figure 7

DISCUSSION

Upon viral infection, the host cell's metabolic processes undergo alterations that depend on the specific virus and the type of host cell involved (Li et al., 2021). The use of transcriptomics, proteomics, metabolomics, and various other omics technologies has become crucial for biomarker screening, diseases diagnosis, and pathogenesis research (Yu et al., 2022). In this research, we performed metabolomics and transcriptomics sequencing on DF1 cells infected with H9N2 AIV. The objective was to identify distinct metabolites and genes with altered expression to provide a more comprehensive and precise understanding of the cellular response to the virus. In both positive and negative ion modes, metabolomics analysis identified 120 and 65 metabolites, respectively, showing significant differences. These differential metabolites mainly include amino acids, peptides and their analogues, and glycerophocholines. Amino acids are essential for cellular function, contributing not only to proteins synthesis but also to cell growth and proliferation (Zeng et al., 2022). Numerous metabolomics studies have shown that viral infection can alter amino acid metabolism. For instance, HSV1 infection in KMB17 cells significantly affected the amino acid metabolic pathway, specifically influencing the production of arginine, valine, leucine, and isoleucine (Huang et al., 2023). Moreover, research has found that metabolites in MDCK cell infected with H3N2 canine influenza virus were concentrated in metabolic pathways related to cysteine and methionine metabolism, arginine and proline metabolism, and amino acid biosynthesis (Tao et al., 2020).

This study also employed transcriptomics technology to analyze the significantly differential gene expression in DF1 cells 12 h after H9N2 AIV infection. A total of 3,921 genes exhibited significant differential expression, with 3,313 genes upregulated and 608 genes downregulated. Based on metabolomics analysis, the transcriptomics data revealed that the genes IDH1, GPX8, GSTT1, GSTA3, ODC1, CHAC1, RRM2B, ANPEP, GGT1, GGT5, GSS genes were enriched in the glutathione metabolic pathway. Specifically, IDH1, GPX8, CHAC1, ANPEP, GGT1, GGT5 and GSS are closely related to the synthesis and degradation cycle of glutathione. GPX8, part of the GPx family, plays a significant role in antioxidation (Chen et al., 2020). Glutathione reductase (GR) or glutamate cysteine ligase (GCL) and GSS convert the oxidized form of GSH within the cell (Kalinina and Gavriliuk, 2020). GGT1 breaks down glutathione into glutamate and Cys-Gly dipeptide which is subsequently degraded by the membrane-bound enzyme Anpep, producing cysteine and glycine for the next glutathione cycle (Liu et al., 2021). In the cytoplasm, glutathione can be degraded through CHAC1 (Lv et al., 2019). Previous research indicated that exposure to PM 2.5 increased the levels of GSH and GSSG in cells, with upregulation in genes encoding enzymes involved in glutathione metabolism, such as IDH1, CHAC1, and ANPEP, in TM4 cells (Shi et al., 2022). This study found that influenza virus infection increased the expression levels of CHAC1, ANPEP, GGT1, and GGT5 genes, suggesting enhanced breakdown of intracellular glutathione following H9N2 AIV infection. However, the decreased expression level of GSS gene indicated reduced intracellular glutathione synthesis. These findings suggested that H9N2 AIV infection in DF1 cells leads to increased expression of genes associated with glutathione breakdown and decreased expression of genes related to glutathione production. Additionally, BSO treatment significantly reduced the expression levels of GGT5, GPX4 and ANPEP mRNA level compared with Flu group. These results indicated that BSO treatment can alter gene expression involved in the decomposition, synthesis, and consumption of intracellular glutathione, thereby affecting intracellular glutathione content, viral replication, and cellular redox status.

In this study, virus infection significantly upregulated intracellular IL-1β and IFN-γ mRNA expression levels, and significantly down-regulated intracellular IL-13 and TGF-β mRNA expression levels after BSO treatment. Cytokine IL-1β and IFN-γ are key mediator of the inflammatory response, capable of inducing adaptive immune response and aiding in virus clearance against influenza (Kronstad et al., 2018). IL-13 has the ability to antagonize Th1 driven pro-inflammatory immune responses and can downregulate the synthesis of many pro-inflammatory cytokines, including IL-1, IL-6 (Iwasaki and Pillai, 2014). One study found that pretreatment of A549 cell with TGF-β1, as an antiviral factor before infection with IAV, reduced caspase-1 activation and IL-1β production (BustosRivera-Bahena et al., 2021). In this experiment, viral infection after BSO treatment significantly down-regulated intracellular Nrf2, CAT, NQO1, and HO-1 mRNA expression levels. A previous study has found that Arctiin, a biologically active lignan glycoside, could inhibit H9N2 AIV-mediated inflammation in A549 cell by activating Nrf2/HO-1 signaling pathway (Zhou et al., 2021).

Furthermore, our study also found that BSO treatment significantly downregulated intracellular GSH levels and CAT activity after viral infection, significantly increased intracellular MDA and ROS content, but had no effect on SOD activity. GSH is a critical antioxidant in cells, playing a vital role in reducing oxidative stress by neutralizing free radicals and ROS. It exists in GSH and GSSG forms, with the reduced form being the active antioxidant. Key antioxidant enzymes include SOD, CAT, and glutathione peroxidase (GPx). These enzymes catalyze reactions that detoxify ROS, thus protecting cells from oxidative damage. In this experiment, our results found that when DF1 cells were infected with H9N2 AIV, the intracellular GSH content increased, but the activity of antioxidant enzymes decreased. Here is our explanation for this phenomenon: (1) Compensatory mechanism: The increase in GSH might be a compensatory response to counteract the oxidative stress caused by the viral infection. When antioxidant enzyme activities are downregulated, the cell may enhance GSH synthesis to maintain redox balance (Birben et al., 2012). (2) Independent regulation pathways: The pathways regulating GSH levels and antioxidant enzymes might be independently controlled. Specific cellular signals or stressors could selectively upregulate GSH synthesis while downregulating antioxidant enzyme expression or activity (Forman et al., 2008). (3) Temporal dynamics: The time point at which measurements were taken might capture a transient state where GSH levels initially increase to combat oxidative stress, while the downregulation of antioxidant enzymes is a subsequent response (Birben et al., 2012). (4) Feedback mechanisms: Accumulation of GSH might negatively regulate antioxidant enzyme expression through feedback mechanisms. If the cellular environment shifts towards a less oxidative state, it could signal that the demand for antioxidant enzymes is lower (Lu, 2013). Therefore, our research provided that H9N2 infection after BSO treatment can exacerbate AIV induced oxidative stress in DF1 cells.

CONCLUSIONS

Metabolomics analysis revealed a significant upregulation of glutathione content in DF1 cells following H9N2 AIV infection. Combined transcriptomics and metabolomics analysis identified 11 differentially expressed metabolic genes and 3 differentially expressed metabolites that exhibit interactive regulatory effects within the glutathione metabolism pathway. Treatment of DF1 cells with the glutathione synthesis inhibitor BSO significantly reduced intracellular GSH levels, which in turn markedly enhanced H9N2 AIV replication upon infection. Post BSO treatment, viral infection increased the expression of pro-inflammatory cytokines, weakened cellular antioxidant capacity, and exacerbated inflammation and oxidative stress induced by AIV infection, thereby promoting H9N2 AIV replication in DF1 cells. Therefore, future research can explore the universality of the glutathione metabolism pathway in different types of cells and viral infections, particularly its application in other avian influenza virus strains and host cells. Additionally, studying methods to regulate GSH levels to develop new antiviral strategies and enhance the antiviral capacity of host cells has significant practical implications. In-depth analysis of the specific mechanisms of the Nrf2/HO-1 pathway in viral infections and host cell responses can provide a theoretical basis for designing more effective antiviral drugs and treatments. The research findings not only offer new insights into the prevention and control of avian influenza viruses but also provide important scientific evidence for understanding the mechanisms of virus-host cell interactions.

Appendix Supplementary materials

Figure S1. PCA and PLS-DA scores of samples in each group (n = 3). (A) PCA analysis in positive ion mode; (B) PCA analysis in negative ion mode; (C) PLS-DA analysis in positive ion mode; (D) PLS-DA analysis in negative ion mode.

Image, application 1

ACKNOWLEDGMENTS

This work was supported by the National Natural Science Foundation of China: 32272992 ; the Xinjiang Animal Disease Prevention and Control Engineering Technology Research Center:GC2021003 .

DISCLOSURES

The authors declare no conflicts of interest.

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.psj.2024.104125.
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