Ye XY, He XZ, Hu ZT, Zheng FF, Huang XG, Xie XM, Chen FH, Ou HB, Qiu RX. Integrated transcriptomics and metabolomics reveal neutrophil extracellular trap associated with interferon treatment for chronic hepatitis B. World J Hepatol 2026; 18(4): 114804 [DOI: 10.4254/wjh.v18.i4.114804]
Corresponding Author of This Article
Rong-Xian Qiu, Department of Infectious Diseases, The Affiliated Hospital of Putian University, No. 181 Meiyuan East Road, Putian 351100, Fujian Province, China. 18850959988@163.com
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Gastroenterology & Hepatology
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Basic Study
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Apr 27, 2026 (publication date) through Apr 22, 2026
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World Journal of Hepatology
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1948-5182
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Ye XY, He XZ, Hu ZT, Zheng FF, Huang XG, Xie XM, Chen FH, Ou HB, Qiu RX. Integrated transcriptomics and metabolomics reveal neutrophil extracellular trap associated with interferon treatment for chronic hepatitis B. World J Hepatol 2026; 18(4): 114804 [DOI: 10.4254/wjh.v18.i4.114804]
Author contributions: Ye XY, Ou HB, and Qiu RX designed experiments, data analyses, and wrote the manuscript; Ye XY, He XZ, and Hu ZT performed experiments and collected data; Zheng FF, Huang XG, Xie XM, and Chen FH provided technical support and collected data; and all authors have reviewed the manuscript.
Institutional review board statement: This work was approved by the Human Research Ethics Committee in the Affiliated Hospital of Putian University (No. PYFL202416).
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: Datasets are hosted in public repositories Science data bank under accession number 26078 and are available at the following URL: https://doi.org/10.57760/sciencedb.26078.
Corresponding author: Rong-Xian Qiu, Department of Infectious Diseases, The Affiliated Hospital of Putian University, No. 181 Meiyuan East Road, Putian 351100, Fujian Province, China. 18850959988@163.com
Received: September 29, 2025 Revised: November 11, 2025 Accepted: January 9, 2026 Published online: April 27, 2026 Processing time: 205 Days and 4.9 Hours
Abstract
BACKGROUND
Chronic hepatitis B (CHB) is a significant global health issue, and interferon (IFN) is one of the main first-line therapies for CHB.
AIM
To investigate the altered transcriptome, metabolites, and their correlations, as well as the effects and mechanisms of IFN treatment for CHB.
METHODS
The patients received peginterferon alfa-2b at a dose of 180 μg for 0, 1, 3, and 6 months and serum samples were collected for clinical biological assays, transcriptomics, and metabolomics analyses.
RESULTS
The results showed that IFN-related immune pathways and neutrophil extracellular traps (NETs) were the most significantly altered pathways following IFN treatment. Correlation analysis revealed a strong link between immune system-related genes in the transcriptome and dipeptides in the metabolome during IFN-α treatment. Notably, core components of NETs, including histones H2A clustered histone 14, H2B clustered histone 5, H3 clustered histone 1, and H4 clustered histone 4, were significantly increased after IFN treatment. The positively charged histones may bind to the negatively charged viral envelope, potentially enhancing the antiviral effect.
CONCLUSION
Integrated non-targeted transcriptomic and metabolomic analyses to CHB patients undergoing IFN-α treatment revealed that IFN-related immune pathways and NETs were the most significantly affected pathways during IFN treatment. These findings provide a deeper understanding of the role of NETs and dipeptides in the antiviral response to IFN treatment for CHB.
Core Tip: Chronic hepatitis B is a significant global health issue, and interferon (IFN) is one of the main first-line therapies for chronic hepatitis B. Non-targeted transcriptomic and metabolomic analyses revealed that IFN-related immune pathways and neutrophil extracellular trap formation were the most significantly altered pathways after IFN treatment. Core components of neutrophil extracellular traps including histones were notably increased and potentially enhance the antiviral response.
Citation: Ye XY, He XZ, Hu ZT, Zheng FF, Huang XG, Xie XM, Chen FH, Ou HB, Qiu RX. Integrated transcriptomics and metabolomics reveal neutrophil extracellular trap associated with interferon treatment for chronic hepatitis B. World J Hepatol 2026; 18(4): 114804
Hepatitis B virus (HBV), a member of the Hepadnaviridae family, primarily infects the human liver and places patients at high risk of cirrhosis and liver cancer, leading causes of death among those with chronic hepatitis B (CHB)[1]. In 2019, CHB posed a significant global health challenge, with approximately 1.5 million new cases and 0.8 million deaths worldwide. Upon infection, the genomic relaxed circular DNA of HBV is converted into covalently closed circular DNA (cccDNA), which assembles into a minichromosomal structure. The cccDNA can integrate into the host genome and serve as a template to express the HBV surface antigen (HBsAg). HBsAg has been implicated in inducing HBV-specific immune tolerance, contributing to the persistence of the infection[1,2]. Liver damage associated with HBV infection results from persistent necrotizing inflammation. HBsAg is known to impair T cell responses, results in T cell exhaustion and the elimination of T cell that recognize specific epitopes. This dysfunctional immune response ultimately progresses to liver fibrosis, cirrhosis, and hepatocellular carcinoma. Vaccination against HBV at birth remains the primary strategy for eliminating hepatitis B and reducing the incidence of liver-related complications. However, vaccines have proven ineffective as therapeutic agents in treating chronic infection[1,2].
At present, pegylated interferon (peg-IFN) and nucleos(t)ide analogs (NA) are the two main first-line therapies for CHB. Interferon (IFN) monotherapy triggers both innate and adaptive immune responses, exerting modest antiviral effects[3,4]. It induces the expression of numerous interferon-stimulated genes, which regulate various steps of HBV replication[5]. For example, interferon-stimulated genes inhibit cccDNA transcription through epigenetic silencing and transcriptional suppression, contributing to the decay of cccDNA and HBV[4]. Studies have also shown that the cytidine deaminase APOBEC3A, activated by IFN-α, induces cytidine deamination, leading to the degradation of cccDNA[6]. IFN-α can further control HBV replication by activating host innate immune cells (e.g., natural killer cells) and adaptive immune cells (e.g., T helper and B cells)[3]. In contrast, NA compete with natural nucleotides for binding sites on reverse transcriptase, effectively suppressing HBV replication but not affecting cccDNA. Therefore, relapse rates are high once NA treatment is discontinued, and long-term NA therapy is generally required. However, the cure rate for peg-IFN-α treatment remains limited[4]. Moreover, IFN therapies are relatively expensive and may resulted in significant side effects, such as hepatitis flares, liver decompensation, and other toxicities, which sometimes necessitate treatment interruption[7,8]. The underlying mechanisms of IFN treatment for CHB remain incompletely understood, highlighting the need for deeper insights into its physiological and pathological effects. Recently, systems-level transcriptomic approaches have become powerful tools in biomedical research. Most transcriptomic studies on CHB have focused on identifying potential biomarkers and monitoring disease progression[9,10]. However, little is known about the transcriptomic profiles related to therapeutic responses, particularly those induced by IFN treatment. This study aims to investigate the changes in transcriptomic and metabolomic profiles in the serum of CHB patients undergoing IFN treatment. By examining the altered transcriptome, metabolites, and their correlations, this research explores the effects and mechanisms of IFN treatment for CHB.
MATERIALS AND METHODS
Subjects
Patients with chronic HBV infection were recruited from the Affiliated Hospital of Putian University between August 2023 and January 2024. The study was conducted in accordance with good clinical practice and approved by the Medical Ethics Committee of the Affiliated Hospital of Putian University (Approval No. PYFL202416). All enrolled patients provided written informed consent. The diagnostic criteria for CHB followed the “Guidelines for the Prevention and Treatment of Chronic Hepatitis B” issued in 2015[11]. The patients received weekly subcutaneous injections of peg-IFN alfa-2b (Xiamen Tebao, Xiamen, Fujian Province, China) at a dose of 180 μg for 0, 1, 3, and 6 months. The patients were divided into four groups: The untreated group: (1) With 20 cases, the 1-month IFN treatment group; (2) With 18 cases, the 3-month IFN treatment group; (3) With 20 cases, the 6-month IFN treatment group; and (4) With 20 cases. Serum samples were collected at the end of the IFN-α treatment course for clinical biological assays, transcriptomics, and metabolomics analyses. The clinical information and biochemistry indicators of the patients are listed in Table 1.
Inclusion criteria: (1) Diagnosis of CHB based on established criteria; (2) Voluntary participation and cooperation with the research; and (3) No antiviral treatment within the past 6 months.
Exclusion criteria: (1) Other viral hepatitis, liver cancer, hereditary diseases, iron deficiency disorders, or previous iron metabolism disorders; (2) Pregnant or lactating women; (3) Complications such as hepato-renal syndrome, severe infections, cerebral edema, or gastrointestinal bleeding; (4) Other serious systemic diseases or mental illnesses; (5) Diabetes or any tumor-related conditions; (6) Participation in other clinical trials within the past 3 months; and (7) Any condition preventing cooperation with the study.
Collection of serum samples and biochemistry assays
Blood samples (5 mL) were collected from patients after an overnight fast. The samples were allowed to coagulate, then centrifuged. The supernatant was centrifuged again, divided into several tubes, and stored at -80 °C. Clinical biochemistry assays were performed by the hospital’s clinical laboratory following standard procedures (Table 1).
Transcriptomics analysis: Freshly isolated peripheral blood mononuclear cells were suspended in TRIzol reagent (Invitrogen, CA, United States) for RNA extraction. RNA quality was assessed, and cDNA libraries were constructed and sequenced using the Illumina PE150 system at Novogene (Tianjin, China). Library quality was verified with Kapa qPCR and the Agilent 4200 TapeStation system. Sequencing reads were aligned to the reference genome using HISAT2. Gene expression levels were quantified as fragments per kilobase of transcript per million mapped reads, with fragments per kilobase of transcript per million mapped reads values ≥ 0.5 considered as expressed. Differentially expressed genes (DEGs) were analyzed for clustering and functional annotation. Genes with a log2 fold change (FC) ≥ 1.0 and a false discovery rate < 0.05 were considered statistically significant. Pathway annotations were performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases[12]. The xCell algorithm was used to assess immune cell infiltration in the peripheral blood across the four groups based on immune scores.
Metabolomics analysis: Metabolomics analysis was performed by Novogene (Tianjin, China). Equal volumes of serum from all samples were pooled to create quality control samples. Metabolite separation was carried out using vanquish ultra-high-performance liquid chromatography with a waters Acquity ultra-performance liquid chromatography ethylene bridged hybrid amide column (2.1 mm × 100 mm, 1.7 μm). Primary and secondary mass spectrometry data were collected using a Thermo Q Exactive HFX mass spectrometer with Xcalibur software. Raw data were converted to mzXML format using ProteoWizard, and peak recognition, extraction, alignment, and integration were performed using the XCMS package in R. The resulting data were matched against the BiotreeDB (V2.1) secondary mass spectrometry database. Multivariate and univariate statistical analysis was conducted using the MetaboAnalystR 2.0 R package, and iPath analysis was used for metabolomic pathway visualization[13].
Data processing: Multivariate and univariate statistical analyses of transcriptomic and metabolomic data were performed using R software. Principal component analysis (PCA) and supervised orthogonal partial least squares discriminant analysis were employed to assess the data. Model validation was carried out using R2X, R2Y, and Q2 values, with 5-fold cross-validation and 200 permutations to evaluate model quality and prevent overfitting. The variable importance in projection was used to rank the contribution of individual variables in the orthogonal partial least squares discriminant analysis model. Differential metabolites were identified based on variable importance in projection values ≥ 1.0 and P values < 0.05. Enrichment pathway analysis for DEGs and metabolites was performed using the KEGG database. For univariate analyses, FC was used to compare metabolites between groups, with a threshold of FC ≥ 1.5 or FC ≤ 2/3. One-way analysis of variance with Bonferroni post hoc testing was applied to analyze multiple groups. DEGs from IFN treatment were further processed using gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), and Mfuzz analyses using R software.
Statistical analysis
All statistical analyses were performed using R software. Pearson correlation analysis was conducted with GraphPad Prism version 7.0 (GraphPad Software). Multiple group comparisons were performed using one-way analysis of variance with Bonferroni post hoc tests. A two-sided P value < 0.05 was considered statistically significant. aP < 0.05; bP < 0.01; cP < 0.001.
RESULTS
Identification of differential gene expression after IFN treatment for CHB
HTSeq, a Python package for analyzing high-throughput sequencing data, was used to calculate read counts and assign reads to specific genes. The total read counts for the four groups were as follows: 46326155 for group A (untreated), 45337491 for group B (one-month IFN treatment), 43280891 for group C (three-month IFN treatment), and 44242048 for group D (six-month IFN treatment). PCA was performed to assess gene expression trends across the groups, revealing clear separation between them (Figure 1A). The separation was also evident between groups B vs A (Figure 1B), C vs B (Figure 1C), and D vs C (Figure 1D). A Venn diagram displayed the overlap and differences in gene expression among the groups (Figure 1E). DEGs were identified using the threshold |log2FC| > 1 and Padj < 0.05, and the results were visualized with volcano plots for B vs A (Figure 1F), C vs B (Figure 1G), and D vs C (Figure 1H).
Figure 1 Identification of differentially expressed genes after interferon treatment for chronic hepatitis B through transcriptomic analysis.
A: Principal component analysis (PCA) of all differentially expressed genes (DEGs) across the four groups: Untreated (group A); 1-month interferon (IFN) treatment (group B); 3-month IFN treatment (group C); 6-month IFN treatment (group D); B: PCA comparing (B vs A); C: PCA comparing (B vs C); D: PCA comparing (C vs D); E: Venn diagram showing the overlap of all DEGs across the four groups; F: Volcano plots of DEGs in the comparisons (B vs A); G: Volcano plots of DEGs in the comparisons (C vs B); H: Volcano plots of DEGs in the comparisons (D vs C). PCA: Principal component analysis.
Enrichment analysis of DEG after IFN treatment for CHB
To investigate the biological processes (BP) and underlying mechanisms of the DEGs following IFN treatment for CHB, we performed GO and KEGG pathway enrichment analyses. GO, a comprehensive database used to describe gene functions[14], revealed that the top altered BP terms in group B vs group A were related to “production of molecular mediator of immune response and cytokine-mediated signaling pathway” (Figure 2A). KEGG pathway analysis showed that the most enriched pathways were “systemic lupus erythematosus (SLE), cytokine-cytokine receptor interaction and NET” in group B vs A (Figure 2B). Pathway maps for “SLE” and “NET” are shown in Figure 2C and D, respectively. In the SLE pathway, notable DEGs included C1qb, C2, and HLADQA1; in the NET pathway, key DEGs included FCGR3b, FPR3, and ITGB3. Histone genes [H2A clustered histone 14 (H2AC14), H2B clustered histone 5 (H2BC5), H3 clustered histone 1 (H3C1), and H4 clustered histone 4 (H4C4)] were prominent in both pathways (Figure 2C and D). “Reactome enrichment analysis also highlighted IFN signaling and G-protein coupled receptor (GPCR) ligand binding” as the top altered pathways in group B vs A (Figure 3A). GSEA identified “IFNbeta targets” as the most enriched terms in group B vs A (Figure 3B). The top five DEGs in this group were interferon-induced protein 44 family member F (IFI44F), IFI44, MX dynamin-like GTPase 1 (MX1), MX2, and oligoadenylate synthetase-like (OASL) (Figure 3). These findings suggest that interferon-related immune pathways and NET are key altered pathways after IFN treatment for CHB.
Figure 2 Enrichment analysis of differentially expressed genes after interferon treatment.
A: Bar chart of Gene Ontology classification enrichment, including the three ontologies: Cellular component, molecular function, and biological process, for the B vs A group; B: Bar chart showing Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the top 20 pathways for differentially expressed genes in the B vs A group; C: KEGG pathway maps for “systemic lupus erythematosus” (KEGG map 05322) and “neutrophil extracellular trap” (KEGG map 04613); D: KEGG pathway maps for “systemic lupus erythematosus” (KEGG map 05322) for the B vs A group [permission from KEGG (12)]. GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; BP: Biological process; CC: Cellular component; MF: Molecular function.
Figure 3 Kyoto Encyclopedia of Genes and Genomes enrichment analysis of differentially expressed genes after interferon treatment.
A: Rectangular tree diagram showing the top enriched differentially expressed genes pathways in the B vs A group; B: Heatmap of differentially expressed genes in the “IFNbeta targets” gene set, based on gene set enrichment analysis for the B vs A group.
WGCNA and Mfuzz analysis of DEG after IFN treatment for CHB
WGCNA was used to describe gene association patterns among the IFN treatment groups. This method clusters highly correlated genes into modules, revealing 21 modules in the B vs A dataset. The most significant module was MEbrown (correlation = -0.76, P = 2 × 10-15) (Figure 4A). The top 14 genes in the MEbrown module, including IFIs, OSAL, and C1Qs, were primarily associated with interferon-related immune and NET pathways (Figure 4B). We also analyzed the longitudinal evolution of DEG across the four periods of IFN treatment using the Mfuzz algorithm, which identified 30 clusters (Supplementary Figure 1). Clusters 5, 6, 9, 10, 16, and 19 showed minimal fluctuations, whereas clusters 9 and 10 exhibited notable changes and strong peaks (Figure 4C). Interestingly, genes in Mfuzz_cluster 9, such as C2, H2AC14, and H2BC5, were associated with the “SLE and NET” pathways (Figure 4D). Genes in cluster 10, including IFI44, IFN-induced protein 44-like (IFI44 L), MX1, and MX2, were related to interferon pathways (Figure 4D). These analyses support the conclusion that, besides the IFN pathway, the NET pathway is prominently altered after IFN treatment.
Figure 4 Weighted gene co-expression network analysis and Mfuzz analysis of differentially expressed genes after interferon treatment.
A: Weighted gene co-expression network analysis showing the correlation between modules and the four groups; B: List of module members with the top gene significance; C: Mfuzz pseudo-time analysis clustering of differentially expressed genes in clusters 9 and 10 based on metabolic network pathways; D: List of genes related to clusters 9 and 10.
Immune infiltration analysis after IFN treatment for CHB
Enrichment analyses suggested that the immune system plays a critical role in IFN treatment for CHB. To assess immune cell infiltration, we applied the xCell algorithm to evaluate immune infiltration scores across the four groups. The analysis revealed significant alterations in T cells (CD8+, central memory CD8+, effector memory CD8+) and neutrophils (Figure 5), indicating that T cells and neutrophils were the most differentially expressed immune cells after IFN treatment. The increased neutrophil infiltration correlated with activation of the NET pathway.
Figure 5 Immune infiltration analysis of blood samples across all four groups after interferon treatment for chronic hepatitis B.
a: Indicates the significantly altered immune cell subtypes.
Top differential metabolites after IFN-α treatment
Univariate analysis of differential metabolites, based on Human Metabolome Database classification and the criteria (FC ≥ 1.5 or ≤ 0.67, P < 0.05), revealed significant metabolic changes. Hierarchical clustering analysis of the top 20 differential metabolites in group B vs A is shown in Figure 6A. Radar plots of group B vs A identified the top metabolites with the highest P value, including Cytidine, Phe-Trp, Phe-Leu, and Val-Ile (Figure 6B). KEGG enrichment analysis of the differential metabolites revealed that the top 3 altered pathways were ATP-binding cassette transporter, biosynthesis of amine acid, and protein digestion and absorption (Figure 6C). The bar chart (Figure 6D) showed that the top affected metabolic pathways included protein digestion and absorption, caffeine metabolism, and the biosynthesis of valine, leucine, and isoleucine. iPath analysis, a web-based tool for visualizing biochemical pathways, provided further insights into the global metabolic response to IFN treatment[13], with red lines indicating upregulated pathways (Supplementary Figure 2). Notably, lipid metabolism (Figure 6E) and amino acid metabolism (Figure 6F) were prominently upregulated, consistent with the changes observed in protein digestion, caffeine metabolism, and amino acid biosynthesis.
Figure 6 Metabolomics analysis after interferon treatment.
A: Hierarchical clustering analysis of the top 20 differential metabolites in the B vs A group; red represents upregulation, and blue represents downregulation; B: Radar plot showing the top 10 differential metabolites in the B vs A group; C: Enrichment analysis of differential metabolites in the B vs A group; Kyoto Encyclopedia of Genes and Genomes classification chart; D: Enrichment analysis of differential metabolites in the B vs A group; bar chart of the top 15 enriched Kyoto Encyclopedia of Genes and Genomes pathways; E: Parts of the iPath analysis for metabolites in the B vs A group, including lipid metabolism and metabolism of other amino acids; F: Parts of the iPath analysis for metabolites in the B vs A group, including lipid metabolism; red lines indicate upregulated pathways.
Correlation of DEGs and differential metabolites after IFN-α treatment
Multi-omics analysis allows for the integration of various interconnected components in biological systems to better understand the mechanisms behind complex biological processes. In this study, we evaluated the potential correlation between transcriptomics and metabolomics datasets using Pearson correlation analysis for the B vs A group (r > 0.8; P < 0.05) (Figure 7). The analysis revealed strong correlations between DEGs, including ADAM metallopeptidase with thrombospondin type 1 motif 5, ADGRB3.DT, ALF transcription elongation factor 2, A-kinase anchoring protein 12, calcium voltage-gated channel auxiliary subunit gamma 6, free fatty acid receptor 2, follistatin like I, heparin binding EGF like growth factor, HECT and RCC-like domain-containing protein 5, hydroxytryptamine receptor 1B, integrin subunit beta 8, METTL7B, MT.ATP8, OOSP4B, PLPPR4, PTENP1.AS, SPARC (osteonectin), Cwcv and Kazal like domains proteoglycan 3, and thrombospondin type 1 domain containing 7A. As shown in Table 2, these genes are primarily associated with cytoplasmic membrane proteins, signaling transduction, and the extracellular matrix. The metabolites that showed strong correlations with these DEGs included ciprofibrate, dimethoxy-pyridinylbenzamide, hydroxyquinazolin-yl-propanamide, Ile-Leu, leucylphenylalanine, Leu-Ile, Leu-Leu, Leu-Val, nitrobenzylidnamino-2-oxazolidinone, nitrofurazone, nonadienoylcarnitine, Phe-Leu, Phe-Trp, prolylhydroxyproline, pyrimidine-diydiilmono-dibenzoic acid, pyripyropene A, quizalofop-ethyl, Ile-Val, and Val-Ile. These findings suggest a strong correlation between cytoplasmic membrane protein-mediated signaling transduction and dipeptide metabolism.
Figure 7 Correlation between differentially expressed genes and differential metabolites after interferon alpha treatment.
Pearson correlation analysis of the datasets from transcriptomics and metabolomics in the B vs A group. aP < 0.05, bP < 0.01, cP < 0.001.
Table 2 Gene Ontology function of some genes related to integrated study.
In this study, non-targeted transcriptomics and metabolomics analyses were applied to CHB patients undergoing different durations of IFN treatment (untreated, 1-month treatment, 3-month treatment, and 6-month treatment). KEGG enrichment analysis of transcriptomics data identified the top differential pathways as “SLE and NET” in the IFN-treated groups. GSEA revealed that interferon-related immune pathways were significantly altered, with the top five DEGs being IFI44F, IFI44, MX1, MX2, and OASL. In the metabolomics analysis, the top differential metabolites identified were cytidine, Phe-Trp, Phe-Leu, and Val-Ile in the B vs A group. KEGG enrichment analysis of the metabolites showed that the most affected metabolic pathways were “protein digestion and absorption, biosynthesis of amine acid and valine, leucine and isoleucine biosynthesis”. Correlation analysis between DEGs and differential metabolites suggested a strong relationship between the immune system and dipeptide metabolism.
As expected, the IFN response pathways were the most prominent in the IFN-treated groups, and GSEA analysis confirmed that the top five DEGs were IFI44, IFI44 L, MX1, MX2, and OASL. These genes are part of the ISG family, which plays key roles in antiviral responses by inducing proteins that target various steps of viral replication and block the spread of infection[15]. IFI44 and IFI44 L share a high degree of homology and have been shown to inhibit respiratory syncytial virus replication in vitro[16]. They upregulate IFN-like proteins and STAT, which play a role in the antiviral immune response[17]. These genes also defend against bacteria, viruses, and tumors and have potential as biomarkers or therapeutic targets[17]. MX1 and MX2 are two IFN-inducible dynamin-like GTPases that suppress a wide range of DNA and RNA viruses. MX1 exerts antiviral effects by disrupting viral replication, including transcription, translation, assembly, and transport of viral components in host cells[15]. MX2 specifically inhibits the nuclear import of the viral complex, with its antiviral potency dependent on the viral capsid protein. Resistance to MX2 has been observed in mutants of the HIV-1 capsid[18]. OASL belongs to the oligoadenylate synthetase family (OAS1–3, OASL) and functions in viral sensing and IFN amplification. Human OASL enhances RIG-I activation, sensitizing RIG-I to viral RNA and promoting IFN induction and downstream antiviral responses[19]. Collectively, these data reinforce that IFN-driven pathways are central to the host response to IFN treatment.
Interestingly, the top differential pathways also included “SLE and NET”. Neutrophils are the most abundant leukocytes in the body and act as the first line of defense against infective pathogens by releasing NETs. These traps consist of extruded nuclear chromatin decorated with histones, granular proteins, and antimicrobial proteins such as neutrophil elastase and myeloperoxidase[20]. NETs can capture and neutralize viral particles, facilitate immune cell communication, and enhance complement activation in a physiological state[21]. The web-like structure of NETs, enriched with positively charged histones, can bind and immobilize viral particles, partly through electrostatic attraction, as viral envelopes are negatively charged[22]. In addition to histones, other antimicrobial molecules in NETs, such as myeloperoxidase, defensins, and cathelicidins, help inactivate viral particles[23]. NETs also promote an antiviral adaptive immune response by activating T lymphocytes and other immune cells[24]. This mechanism prevents the virus from spreading to other tissues and protects host cells against a variety of viral pathogens, including respiratory syncytial virus, influenza virus, dengue virus, and even HIV[25].
In the present study, we observed that the core components of NETs, including histones H2AC14, H2BC5, H3C1, and H4C4, were significantly increased after 1 month of IFN treatment compared to the untreated group (B vs A). These histones, enriched with positively charged amino acids, are attracted to the viral envelope, which is negatively charged. Histones H3 and H4 have been shown to inactivate seasonal influenza A particles and HIV-1[22,26]. IFN-α can prime mature neutrophils to form NETs[27], which in turn activate plasmacytoid dendritic cells to produce more IFN-α in a positive feedback loop[28]. In IFN-deficient mice, reduced NET formation was observed, whereas recombinant IFN-β treatment restored NET production[29,30]. However, excessive NET formation has been associated with exacerbating pathophysiology by promoting inflammation, tissue damage, and thrombosis[31,32]. Recent studies suggest that NETs contribute to the pathogenesis of HBV-related acute-on-chronic liver failure[33], and elevated plasma NET levels correlate with poor outcomes in acute liver failure cases[34]. In our immune infiltration analysis, neutrophils were among the most significantly altered immune cells after IFN treatment, further supporting the role of NETs in the IFN response to HBV.
Metabolomics analysis revealed that the top differential metabolites in the B vs A group were cytidine, Phe-Trp, Phe-Leu, and Val-Ile. Integrated transcriptomic and metabolomic analyses showed a strong correlation between DEGs and metabolites, particularly those related to cytoplasmic membrane protein-mediated signaling and dipeptide metabolism after IFN-α treatment. These DEGs are primarily related to integrin binding, adenylate cyclase binding, GPCR activity, and extracellular matrix-related proteins. A variety of stimuli, including chemokines and cytokines, trigger the activation of integrins, Fcγ receptors, and GPCRs, which in turn induce neutrophil degranulation responses[35]. Activation of GPCRs generates DAG and IP3, leading to an increase in cytosolic calcium, which activates neutrophil degranulation and NET formation. Integrins, part of a superfamily of cell adhesion receptors, bind to extracellular matrix ligands and mediate various immune cell activities, including the NET response[36].
Interestingly, dipeptides containing aromatic amino acids and leucine, such as Tyr-Leu, Phe-Leu, and Trp-Leu, have been shown to exhibit anxiolytic-like activity in mice[37]. Additionally, the antioxidant properties of these dipeptides contribute to anti-inflammatory effects through redox regulation of signaling pathways, such as nuclear factor erythroid 2-related factor 2 (Nrf2)[38]. Nrf2 regulates the redox pathway and many antioxidant defense genes, and evidence suggests that inhibition of NET formation depends on Nrf2-mediated reactive oxygen species inhibition[39]. Several studies also support the role of dipeptides in antiviral activity. For example, metabolomics research identified specific dipeptides in the blood plasma and feces of HIV-1 elite controllers[40]. Among these, Aly glutamine and tryptophylglycine demonstrated the highest anti-HIV-1 activity. These dipeptides were effective against all HIV-1 subtypes, regardless of co-receptor usage, and exhibited low cytotoxicity[41]. Furthermore, dipeptide mimetics, such as Matijin-Su derivatives, have been synthesized as anti-HBV agents, with promising results in anti-HBV activity[42]. Recent studies have also highlighted the potential of dipeptides as biomarkers for various diseases. For example, the concentration of γ-glutamyl dipeptides in serum fluctuates across nine types of liver diseases, including hepatitis, cirrhosis, and hepatocellular carcinoma, suggesting their potential for liver disease screening[43]. However, research on dipeptides as functional biomarkers for diseases is still emerging and requires further investigation.
Despite these findings, this study has several limitations. Although our results suggest that NETs and dipeptides may play a role in anti-HBV immunity, experimental validation is still needed. Additionally, the sample size in this study was relatively small, and future research with larger sample sizes will be necessary to gain a deeper understanding of the mechanisms behind NETs and dipeptides in the response to IFN treatment.
CONCLUSION
This study applied integrated non-targeted transcriptomic and metabolomic analyses to CHB patients undergoing IFN-α treatment. KEGG enrichment analysis revealed that IFN-related immune pathways and NETs were the most significantly affected pathways during IFN treatment for CHB. Correlation analysis of DEGs and metabolites after IFN-α treatment showed a strong relationship between cytoplasmic membrane protein-mediated signaling and dipeptide metabolism. The study also found that core components of NETs, including histones H2AC14, H2BC5, H3C1, and H4C4, were increased following IFN treatment. These positively charged histones may bind to the negatively charged viral envelope, potentially enhancing the antiviral response. Overall, this study provides valuable insights into the role of NETs and dipeptides in IFN treatment for CHB, deepening our understanding of their role in the antiviral effects of IFN.
ACKNOWLEDGEMENTS
We appreciate all the authors who have made efforts in the whole program.
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Footnotes
Peer review: Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Gastroenterology and hepatology
Country of origin: China
Peer-review report’s classification
Scientific quality: Grade B, Grade B, Grade C
Novelty: Grade A, Grade B, Grade B
Creativity or innovation: Grade A, Grade B, Grade C
Scientific significance: Grade B, Grade B, Grade C
P-Reviewer: Bai HR, Professor, China; Liu XF, PhD, China S-Editor: Jiang HX L-Editor: A P-Editor: Wang CH
