Transcriptomic profiling of HBV transgenic mouse livers reveals ZBP1-associated necroptotic signaling

Abstract

Although transcriptome studies have been performed in cell culture models of HBV infection, the in vivo hepatic transcriptional response to persistent HBV expression remains incompletely characterized. In addition, whether HBV is associated with activation of necroptotic signaling in the liver has not been fully clarified. Therefore, this study aimed to: (1) define transcriptomic alterations in livers from an HBV transgenic mouse model; and (2) explore whether ZBP1-associated necroptotic signaling is present in HBV-Tg livers by integrating RNA-seq, bioinformatic analysis, and biochemical validation. We utilized an HBV transgenic mouse model and characterized it by measuring serum HBV DNA, HBsAg, AST, ALT, and TBIL levels and by performing hematoxylin and eosin (H&E) staining. Subsequently, the expression of protein-coding genes in HBV transgenic and control mice was analyzed by next-generation RNA sequencing (RNA-seq). Differentially expressed genes (DEGs) were identified using EdgeR with thresholds of |log2(fold change)| > 1 and P value < 0.05. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the DEGs. A protein-protein interaction (PPI) network was constructed based on the STRING database, and PPI modules were analyzed using the MCODE plugin in Cytoscape. Finally, we evaluated ZBP1-associated necroptotic signaling by examining the protein levels of ZBP1 and phosphorylated RIPK3/MLKL by Western blot. We identified 815 candidate differentially expressed genes, including 412 upregulated and 403 downregulated genes in HBV-Tg livers compared with control livers. KEGG pathway analysis indicated enrichment of immune-related pathways, metabolic pathways, and viral infection-related pathways. Zbp1 drew further attention because it was increased in HBV-Tg livers and located within an interferon/innate immune-related PPI module. At the protein level, ZBP1, p-RIPK3, and p-MLKL were increased in HBV-Tg livers. These findings suggest elevated ZBP1-associated necroptosis-related markers in the HBV-Tg liver model. We profiled gene expression in livers from HBV-Tg and control mice using RNA-seq and identified candidate DEGs, GO terms, and pathway terms associated with HBV-related liver responses. Our data support an association between HBV-Tg liver status and increased ZBP1, p-RIPK3, and p-MLKL expression. However, additional functional studies are required to determine cell specificity, causal direction, and the contribution of this signaling axis to liver injury.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-55138-z.

Introduction

Chronic hepatitis B virus (HBV) infection remains a major global health burden. According to recent World Health Organization (WHO) estimates, 254 million people were living with chronic HBV infection in 2022, and HBV was responsible for approximately 1.1 million deaths, mainly due to cirrhosis and hepatocellular carcinoma¹. The current WHO Global Health Sector Strategies for 2022–2030 continue to frame viral hepatitis elimination as a major public health goal by 2030². A central obstacle to HBV cure is the persistence of covalently closed circular DNA (cccDNA) in hepatocyte nuclei, which serves as the stable transcriptional template for viral RNAs and sustains chronic viral gene expression³. Although in vitro HBV infection systems have advanced substantially in recent years, they still cannot fully reproduce the complex tissue-level host response to persistent intrahepatic HBV antigen expression in vivo⁴. In this context, HBV transgenic (HBV-Tg) mouse models remain useful for investigating HBV-associated liver pathogenesis at the organismal level. These mice, initially generated on a BALB/c background and containing the complete HBV genome (ayw subtype), provide an in vivo platform for examining hepatic responses to persistent viral antigen expression⁵.

High-throughput RNA sequencing (RNA-seq) has become a powerful tool for defining transcriptomic alterations, identifying candidate biomarkers, and resolving disease-associated molecular pathways. In the HBV field, recent transcriptomic studies and reviews have highlighted the value of transcript-level analyses for understanding viral gene expression, host antiviral programs, and intrahepatic heterogeneity⁶. In addition, recent single-cell and spatial transcriptomic studies have further underscored the value of transcriptome-based analyses for resolving liver heterogeneity and disease-associated states⁷. However, compared with cell-based systems and single-hepatocyte studies, the tissue-level transcriptomic landscape of HBV-Tg livers remains less well characterized, particularly with respect to coordinated inflammatory and cell-death-associated pathways.

In the present study, we applied RNA-seq to profile mRNA expression in liver tissues from HBV transgenic mice. Through integrated bioinformatic analyses, we identified immune-related, metabolic, and nucleic acid sensing-related pathways associated with HBV-Tg liver status. Based on the transcriptomic and network-level findings, we further examined ZBP1 and necroptosis-related proteins. Our data suggest that HBV-Tg livers exhibit increased ZBP1 expression together with elevated p-RIPK3 and p-MLKL levels.

Materials and methods

Animals

SPF BALB/c mice and HBV-transgenic (HBV-Tg) male mice (aged 6–7 weeks) were obtained from the Infectious Disease Center of the 458th Hospital (Guangzhou, China). The HBV-Tg mice were originally generated on a BALB/c background and stably carry the entire HBV genome (genotype ayw)⁵. All experimental procedures were conducted in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals (NIH Publication No. 80 − 23) and were approved by the Ethics Committee of Anhui College of Traditional Chinese Medicine. Mice were euthanized via isoflurane inhalation followed by exsanguination. Liver tissue and blood samples were collected, and serum was separated for subsequent analysis. For RNA-seq, three HBV-Tg mice and three age-matched BALB/c control mice were analyzed (n = 3 per group), corresponding to samples M1, M7, and M9 and C1, C2, and C5, respectively.

Virological and serum biochemical assessment

Serum HBsAg levels were measured using an enzyme-linked immunosorbent assay (ELISA) kit (Kewei Biotech Co. Ltd., China) according to the manufacturer’s instructions. Serum HBV DNA levels were determined by quantitative real-time PCR (qPCR) using an HBV Fluorescence Quantitative PCR Diagnostic Kit (Shanghai Kehua Bio-engineering Co., Ltd., China) according to the manufacturer’s instructions. Serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), and total bilirubin (TBIL) levels were measured using commercial assay kits purchased from Nanjing Jiancheng Bioengineering Institute (Nanjing, China), including the ALT/GPT assay kit (C009-2-1), AST/GOT assay kit (C0010-2-1), and TBIL assay kit (C019-1-1), according to the manufacturer’s instructions.

Histopathological examination

Liver tissues were collected and fixed immediately in 10% formalin following euthanasia. After 48 h of fixation, the samples were embedded in paraffin. Sections were prepared and subjected to hematoxylin and eosin (H&E) staining for general morphological evaluation.

RNA extraction and sequencing

Total RNA was extracted from liver samples using a commercial RNA isolation kit (TR205–200, Tianmo, China) according to the manufacturer’s instructions. After assessing the quantity and quality of the RNA samples, an RNA library was constructed using the VAHTSTM mRNA-seq V2 Library Prep Kit (VAHTS, China). The resulting libraries were quantified using a Qubit 3.0 Fluorometer and an Agilent 2100 Bioanalyzer. Cluster generation was performed using the cBot system, and high-throughput sequencing was carried out on an Illumina platform by Origin-Biotech Inc. (Ao-Ji Biotech, China).

Procedure of bioinformatics analysis

Quality control (QC) of the raw sequencing reads was performed using FastQC⁸. Adapter and low-quality sequences were trimmed using seqtk⁹. Transcript abundance for each library was quantified with kallisto (v0.44.0)¹⁰, using a k-mer index built from the Ensembl reference transcriptome (release 104)¹¹. Gene-level transcripts per million (TPM) and read counts were summarized from transcript-level estimates using the tximport R package¹², with the parameters type = “kallisto” and ignoreAfterBar = TRUE. Candidate differentially expressed genes (DEGs) between HBV-Tg and control groups were identified using edgeR¹³, with thresholds of |log2(fold change)| > 1 and P value < 0.05. The volcano plot was generated using log2(fold change) and P value. For Tables 1 and 2, genes were ranked by P value within each regulation direction, and entries without valid gene symbols were excluded.

Table 1.

Top 20 upregulated candidate DEGs ranked by P value in HBV-Tg mouse livers.

Ensembl ID	Gene Name	log2FC	P value	MGI description
ENSMUSG00000091867	Cyp2a22	2.62	1.59E-17	cytochrome P450, family 2, subfamily a, polypeptide 22
ENSMUSG00000024697	Gna14	2.54	1.55E-10	G protein subunit alpha 14
ENSMUSG00000036560	Lgi4	3.91	3.00E-10	leucine-rich repeat LGI family member 4
ENSMUSG00000015854	Cd5l	1.42	9.85E-10	CD5 antigen-like
ENSMUSG00000078452	Raet1a	4.15	1.02E-09	retinoic acid early transcript 1 alpha
ENSMUSG00000033707	Lrrc24	2.29	1.40E-09	leucine rich repeat containing 24
ENSMUSG00000046805	Mpeg1	1.43	1.22E-08	macrophage expressed 1
ENSMUSG00000021508	Cxcl14	3.39	1.72E-08	C-X-C motif chemokine ligand 14
ENSMUSG00000041653	Pnpla3	2.61	2.31E-08	patatin-like phospholipase domain containing 3
ENSMUSG00000021208	Ifi27l2b	2.15	6.54E-08	interferon alpha-inducible protein 27-like 2B
ENSMUSG00000037071	Scd1	1.31	7.80E-08	stearoyl-CoA desaturase 1
ENSMUSG00000055254	Ntrk2	3.22	9.49E-08	neurotrophic receptor tyrosine kinase 2
ENSMUSG00000032561	Acp3	3.25	1.23E-07	acid phosphatase 3
ENSMUSG00000061132	Blnk	2.98	2.21E-07	B cell linker
ENSMUSG00000045776	Lrtm1	1.54	2.42E-07	leucine-rich repeats and transmembrane domains 1
ENSMUSG00000018566	Slc2a4	2.70	2.65E-07	solute carrier family 2 member 4
ENSMUSG00000034875	Nudt19	1.10	5.71E-07	nudix hydrolase 19
ENSMUSG00000012123	Crybg2	1.72	7.26E-07	crystallin beta-gamma domain containing 2
ENSMUSG00000004730	Adgre1	1.25	8.20E-07	adhesion G protein-coupled receptor E1
ENSMUSG00000015437	Gzmb	2.76	8.40E-07	granzyme B

Table 2.

Top 20 downregulated candidate DEGs ranked by P value in HBV-Tg mouse livers.

Ensembl ID	Gene name	log2FC	P value	MGI description
ENSMUSG00000052776	Oas1a	-11.01	5.26E-35	2’-5’ oligoadenylate synthetase 1 A
ENSMUSG00000090175	Ugt1a9	-3.70	1.28E-33	UDP glucuronosyltransferase 1 family, polypeptide A9
ENSMUSG00000025171	Ubtd1	-3.33	4.07E-19	ubiquitin domain containing 1
ENSMUSG00000066861	Oas1g	-9.51	4.21E-19	2’-5’ oligoadenylate synthetase 1G
ENSMUSG00000094066	Spata31f1b	-8.14	7.65E-16	spermatogenesis associated 31 family member F1B
ENSMUSG00000019737	Syne4	-5.35	3.42E-15	spectrin repeat containing nuclear envelope family member 4
ENSMUSG00000028715	Cyp4a14	-3.08	6.57E-12	cytochrome P450, family 4, subfamily a, polypeptide 14
ENSMUSG00000071633	Gm4952	-1.90	7.22E-11	predicted gene 4952
ENSMUSG00000024924	Vldlr	-2.77	1.58E-10	very low density lipoprotein receptor
ENSMUSG00000032649	Colgalt2	-3.42	2.10E-10	collagen beta(1-O)galactosyltransferase 2
ENSMUSG00000023963	Cyp39a1	-1.48	2.14E-08	cytochrome P450, family 39, subfamily a, polypeptide 1
ENSMUSG00000023044	Csad	-1.92	1.90E-07	cysteine sulfinic acid decarboxylase
ENSMUSG00000074489	Bglap3	-1.27	2.11E-07	bone gamma-carboxyglutamate protein 3
ENSMUSG00000073888	Ccl27a	-1.93	6.14E-07	C-C motif chemokine ligand 27 A
ENSMUSG00000078735	Il11ra2	-3.97	8.74E-07	interleukin 11 receptor alpha 2
ENSMUSG00000035509	Fbxl21	-2.14	1.02E-06	F-box and leucine-rich repeat protein 21
ENSMUSG00000078817	Nlrp12	-1.41	1.09E-06	NLR family pyrin domain containing 12
ENSMUSG00000059060	Rad51b	-1.35	1.11E-06	RAD51 paralog B
ENSMUSG00000026675	Hsd17b7	-1.09	2.76E-06	hydroxysteroid 17-beta dehydrogenase 7
ENSMUSG00000005553	Atp4a	-2.31	3.16E-06	ATPase H+/K+ transporting alpha subunit

To investigate the functional implications of the candidate DEGs, Gene Ontology (GO)¹⁵ and KEGG pathway¹⁶ enrichment analyses were performed using DAVID¹⁷. The top enriched terms in the Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) categories, together with KEGG pathways, were visualized. Functional enrichment networks integrating GO terms and KEGG pathways were constructed using the ClueGO plugin¹⁸ in Cytoscape²⁰.

To further elucidate gene regulatory networks associated with HBV-Tg liver status, candidate DEGs were submitted to the STRING database¹⁹ to retrieve known and predicted protein-protein interactions based on default combined confidence scores. The resulting protein-protein interaction (PPI) network was visualized in Cytoscape²⁰. Functional modules within the PPI network were identified using the Molecular Complex Detection (MCODE) plugin²¹ with default parameters. Hub modules meeting the criteria of an MCODE score > = 5, number of nodes > = 20, and edges > = 100 were selected for further GO and KEGG enrichment analyses using clusterProfiler²². Enriched GO terms and KEGG pathways were visualized according to GeneRatio unless otherwise specified.

Western blot analysis

Western blot analysis was performed using standard procedures. Equal amounts of protein were separated by SDS–PAGE and transferred onto PVDF membranes. The membranes were incubated overnight at 4 °C with the following primary antibodies: anti-ZBP1 (A28338, ABclonal; 1:3,000), anti-phospho-MLKL (AP1550, ABclonal; 1:2,000), anti-phospho-RIPK3 (87148-1-RR, Proteintech; 1:2,000), and anti-GAPDH (A19056, ABclonal; 1:100,000). After washing, the membranes were incubated with HRP-conjugated secondary antibodies. Protein bands were detected using Pierce™ Fast Western ECL Substrate (Pierce, USA) and quantified by densitometric analysis using ImageJ 1.47. The protein expression levels of ZBP1, p-MLKL, and p-RIPK3 were normalized to GAPDH and calculated relative to the corresponding control group.

Statistical analysis

Statistical analyses for non-RNA-seq data were performed using GraphPad Prism (GraphPad Software, USA). Comparisons between two groups were conducted using an unpaired two-tailed Student’s t-test. Differences were considered statistically significant at p < 0.05. Data are presented as mean ± standard deviation (SD).

Results

Characterization of the HBV-Tg mouse model

Serum HBV DNA and HBsAg were assessed in both groups. As expected, neither HBV DNA nor HBsAg was detected in the serum of the control group. In contrast, the HBV-transgenic (HBV-Tg) group showed detectable HBV DNA (1.54 ± 0.21 × 10⁵ copies/mL) and HBsAg (S/N ratio = 5.4 ± 0.64). Compared with the control group, HBV-Tg mice also showed significantly increased serum AST, ALT, and TBIL levels (AST: 91.45 ± 17.13 vs. 46.15 ± 8.43, P < 0.01; ALT: 76.48 ± 16.81 vs. 39.77 ± 3.58, P < 0.01; TBIL: 174.00 ± 53.44 vs. 82.53 ± 29.62, P < 0.01), indicating biochemical liver injury in the model (Fig. 1). Histological examination showed that, compared with control livers, HBV-Tg livers exhibited mild histological abnormalities, including hepatocyte swelling, cytoplasmic rarefaction, and scattered inflammatory cell infiltration (Fig. 2).

Fig. 1.

Characterization of the HBV-Tg mouse model. Serum HBV DNA, HBsAg, AST, ALT, and TBIL levels were measured in control and HBV-Tg mice. HBV DNA and HBsAg were detectable in HBV-Tg mice but not in control mice. Serum AST, ALT, and TBIL levels were increased in HBV-Tg mice compared with control mice. Data are presented as mean ± SD. *P < 0.01 vs. control group.

Fig. 2.

Histological evaluation of liver tissues from control and HBV-Tg mice. Representative hematoxylin and eosin (H&E)-stained liver sections from control and HBV-Tg mice are shown. Compared with control livers, HBV-Tg livers exhibited mild histological abnormalities, including hepatocyte swelling, cytoplasmic rarefaction, and scattered inflammatory cell infiltration.

DEG screening

Candidate differentially expressed genes (DEGs) between the HBV-Tg group and the control group were identified using edgeR. Using the thresholds of |log2(fold change)| > 1 and P value < 0.05, a total of 815 candidate DEGs were identified. Among them, 412 genes were upregulated and 403 genes were downregulated in HBV-Tg livers compared with control livers. The results were visualized using a volcano plot and a hierarchical clustering heatmap, as shown in Fig. 3A and B, respectively. In Fig. 3B, samples M1, M7, and M9 represent HBV-Tg liver samples, whereas C1, C2, and C5 represent control liver samples. The top 20 upregulated and downregulated candidate DEGs ranked by P value are detailed in Tables 1 and 2, respectively. Because Zbp1 was not among the top 20 genes ranked by P value or fold change alone, the transcriptomic and network-level evidence supporting its selection for downstream validation is summarized in Table 3.

Fig. 3.

Differentially expressed genes between HBV-Tg and control mouse livers. (A) Volcano plot of candidate differentially expressed genes screened using |log2(fold change)| > 1 and P value < 0.05. (B) Hierarchical clustering heatmap of candidate differentially expressed genes. M1, M7, and M9 represent HBV-Tg liver samples, whereas C1, C2, and C5 represent control liver samples.

Table 3.

Rationale for prioritizing Zbp1 for downstream validation.

Evidence	Finding	Interpretation
RNA-seq	Zbp1 log2FC = 1.80, P = 9.03E-04	Zbp1 was increased in HBV-Tg livers
Network position	Located in interferon/innate immune-related PPI module	Zbp1 was embedded in an immune/interferon-related module
Neighbor genes	Ifit family genes, Isg15, Irf7, Stat1, Cxcl9/Cxcl10, and Oas family members	Module supported immune/inflammatory relevance
Protein validation	ZBP1, p-RIPK3, p-MLKL increased	Supported elevated necroptosis-associated markers at the protein level
Limitation	No inhibition or genetic manipulation	Association rather than causality

GO functional enrichment analysis of DEGs

To further investigate the functional implications of the 815 candidate DEGs identified between the HBV-Tg and control groups, GO enrichment analysis was performed using DAVID. A total of 134 GO terms were enriched (P < 0.05), including 89 biological process (BP) terms, 16 cellular component (CC) terms, and 29 molecular function (MF) terms. The enriched BP terms were mainly associated with antiviral and immune-regulatory processes, including response to virus, defense response to virus, response to interferon-beta, response to type II interferon, positive regulation of leukocyte activation, chemotaxis, and cell killing. The enriched CC terms were primarily related to receptor complexes, plasma membrane-associated structures, immunological synapse, immunoglobulin complexes, and multivesicular bodies. The enriched MF terms were mainly associated with cytokine receptor binding, chemokine receptor binding, immune receptor activity, heme binding, oxidoreductase activity, and immunoglobulin-related binding. The top enriched GO terms, selected among terms with P < 0.05 and ranked by GeneRatio, are shown in Fig. 4A-C, respectively. To further elucidate the functional relationships among biological processes, a ClueGO network was constructed focusing on the most significant terms (P < 0.001) (Fig. 4D). These analyses indicated that the enriched biological processes were predominantly associated with pathogen response and immune regulation.

Fig. 4.

Gene Ontology enrichment analysis of candidate DEGs. GO enrichment analysis of candidate differentially expressed genes in HBV-Tg mouse livers. The top enriched terms in the biological process (BP), cellular component (CC), and molecular function (MF) categories are shown in (A–C), respectively. (D) ClueGO network analysis showing functional relationships among enriched biological processes. The enriched terms were mainly associated with pathogen response, immune regulation, and inflammatory response.

Pathway enrichment analysis of DEGs

To characterize the major pathways associated with HBV-Tg liver status, KEGG pathway enrichment analysis of the candidate DEGs was performed using clusterProfiler. In total, the candidate DEGs were enriched in 36 KEGG pathways, among which 30 pathways met the threshold of P < 0.05. Among these pathways, the top pathways ranked by GeneRatio included cytokine-cytokine receptor interaction, tuberculosis, phagosome, viral protein interaction with cytokine and cytokine receptor, chemokine signaling pathway, herpes simplex virus 1 infection, Epstein-Barr virus infection, influenza A, cell adhesion molecule interaction, retinol metabolism, and measles. These enriched pathways were mainly associated with immune and inflammatory responses, viral infection-related signaling, cell adhesion, and metabolic regulation. The top enriched KEGG pathways ranked by GeneRatio are shown in Fig. 5.

Fig. 5.

KEGG pathway enrichment analysis of candidate DEGs. KEGG pathway enrichment analysis of candidate differentially expressed genes in HBV-Tg mouse livers. The enriched pathways were mainly associated with immune and inflammatory responses, viral infection-related signaling, cell adhesion, and metabolic regulation.

PPI network

To elucidate the gene regulatory network associated with HBV-Tg liver status, a protein-protein interaction (PPI) network was constructed by submitting the candidate DEGs to the STRING database. The resulting network consisted of 524 nodes and 2,465 interaction pairs (Fig. 6). Among these, 79 genes had degrees greater than 20. The top 10 hub genes ranked in descending order of degree included Ccl2, Ccl5, Cd274, Cxcl10, Cxcl9, Emr1, Irf7, Isg15, Stat1, and Tlr2. Using the MCODE plugin with default parameters, 17 functional modules were identified from the PPI network. Three modules, designated modules 1, 2, and 3, each containing more than 100 edges, were selected for further analysis. Their subnetworks are illustrated in Fig. 7, followed by GO and KEGG pathway enrichment analyses. The network and module-level evidence supporting the prioritization of Zbp1 for downstream validation is summarized in Table 3.

Fig. 6.

Protein-protein interaction network of candidate DEGs. The protein-protein interaction (PPI) network was constructed using candidate differentially expressed genes and visualized in Cytoscape. Node size indicates node degree. Red/pink nodes indicate upregulated genes, and green nodes indicate downregulated genes in HBV-Tg livers compared with control livers.

Fig. 7.

Key PPI modules identified by MCODE analysis. Three representative PPI modules identified from the candidate DEG-based PPI network are shown. These modules were selected based on the number of edges and module scores. Red/pink nodes indicate upregulated genes, and green nodes indicate downregulated genes in HBV-Tg livers compared with control livers. The Zbp1-containing module was associated with immune/interferon-related and antiviral-response-related networks.

HBV-Tg livers show increased ZBP1 expression and elevated necroptosis-associated markers

RNA-seq analysis showed increased Zbp1 mRNA expression in the livers of HBV-Tg mice (log2FC = 1.80, P = 9.03E-04). Although Zbp1 was not among the top 20 candidate DEGs ranked by P value or fold change alone, it was located within a high-scoring interferon/innate immune-related PPI module together with antiviral and inflammatory response genes such as Oas family members, Isg15, Irf7, Stat1, Cxcl9, and Cxcl10. This module-level context suggested that Zbp1 was embedded in an antiviral- and inflammatory-response-related network in HBV-Tg livers. Consistent with the transcriptomic findings, Western blot analysis showed higher protein levels of ZBP1, phosphorylated RIPK3, and phosphorylated MLKL in HBV-Tg livers than in control livers (Fig. 8). These findings suggest increased ZBP1 expression together with elevated necroptosis-associated markers in HBV-Tg livers.

Fig. 8.

Western blot analysis of ZBP1 and necroptosis-associated markers in HBV-Tg mouse livers. Western blot analysis was performed to detect ZBP1, phosphorylated RIPK3 (p-RIPK3), and phosphorylated MLKL (p-MLKL) protein levels in control and HBV-Tg mouse livers. GAPDH was used as the loading control. Quantified protein levels were normalized to GAPDH and expressed relative to the control group. Data are presented as mean ± SD. *P < 0.01 vs. control group.

Discussion

Although hepatic responses to HBV have been studied extensively, transcriptomic profiling of HBV-Tg liver tissue remains valuable because this model captures the tissue-level consequences of persistent intrahepatic viral antigen expression. In that sense, HBV-Tg mice provide a useful framework for studying early pathogenic and pre-neoplastic liver events^23,24. Whole-genome RNA-seq is particularly well suited to this purpose, as it allows pathway-level changes to be identified and biologically connected candidate mechanisms to be prioritized for further study. Against this background, our data provide transcriptomic and biochemical evidence consistent with increased ZBP1 expression and elevated RIPK3/MLKL-associated necroptotic markers in HBV-Tg livers. At the same time, the detectable serum HBV DNA and HBsAg, together with increased serum AST, ALT, and TBIL levels and representative histological abnormalities support the biological validity of the model used for interpreting these transcriptomic changes.

One of the clearest features of the HBV-Tg liver in our dataset was the strong immune and inflammatory signature. GO and KEGG analyses consistently pointed toward pathways related to innate immune responses, cytokine-cytokine receptor interactions, and natural killer cell-mediated cytotoxicity. These enrichment results should be interpreted as pathway-level remodeling rather than uniform upregulation of all genes within these pathways. Taken together, these findings suggest that chronic HBV antigen expression is associated with a broad, integrated tissue-level immune response. Although bulk RNA-seq cannot assign each transcript to a specific cell population, it offers an important advantage in this setting: it captures the overall inflammatory microenvironment shaped by hepatocytes, non-parenchymal liver cells, and infiltrating immune cells. For the present study, this tissue-wide perspective is informative because the biological question concerns the liver response as a whole rather than a single isolated cell type.

An important point to address is the apparent tension between our findings and the idea of HBV as a “stealth virus.” Recent work in primary human hepatocytes has shown that acute productive HBV infection can induce only limited proteomic and secretomic perturbations, supporting the view that HBV may minimize overt cell-intrinsic innate immune activation during early infection²⁵. At the same time, recent reviews have emphasized that HBV actively interferes with innate immune sensing at multiple levels, including viral DNA, RNA, and viral proteins^26,27. Our findings do not necessarily contradict this framework. Rather, they likely reflect a different biological situation: a chronic whole-liver transgenic model characterized by sustained viral antigen expression. In such a setting, prolonged cellular stress, inflammatory crosstalk, and an amplified interferon milieu may make Zbp1-associated signals more apparent, even if those signals remain muted during early or acute infection. From this perspective, the current results do not overturn the stealth-virus concept, but instead suggest that HBV-Tg liver biology may evolve toward a state in which ZBP1 becomes transcriptionally and biochemically visible.

This interpretation also fits with the stage-dependent nature of HBV-Tg liver biology. Previous work has shown that inflammatory transcriptional programs in HBV-Tg mice are not static across the lifespan, but vary with disease stage and age²⁸. Our results are therefore best viewed as reflecting one biologically meaningful phase of the HBV-Tg liver response rather than a universal transcriptomic endpoint applicable to all ages. This stage-related view also helps explain why different HBV-Tg studies have highlighted different pathways without necessarily being in conflict with one another.

The position of Zbp1 within the network analysis further strengthens its relevance. In our dataset, Zbp1 was embedded in the highest-scoring interferon/innate immune-related PPI module, together with classic antiviral response genes from the Oas, Ifit, Isg15, Irf7, and Stat1 families. This suggests that Zbp1 is not an isolated finding, but is embedded in a broader immune-, interferon-, and inflammatory-response-related network in HBV-Tg liver tissue. In transcriptomic studies, this kind of module-level context can be more informative than fold-change ranking alone when selecting candidates for follow-up. In this respect, our work extends previous HBV-Tg omics studies in a complementary way. Barone et al. focused largely on early hepatocarcinogenesis- and growth control-related changes²³, whereas Yang et al. emphasized disturbed lipid metabolism and oxidative stress in HBV-Tg mouse liver²⁴. Our data add another layer to this picture, pointing toward an interferon-associated, ZBP1-linked necroptotic axis within the broader HBV-Tg liver phenotype.

The protein-level data support this interpretation. In parallel with the RNA-seq findings, we observed increased expression of ZBP1 together with elevated p-RIPK3 and p-MLKL levels in HBV-Tg livers. Given the known role of ZBP1 as an upstream sensor capable of linking inflammatory signaling to RIPK3-MLKL activation and broader inflammatory cell-death programs, these concordant transcriptomic and protein changes make the involvement of necroptosis-associated signaling biologically plausible in this model^29,30. Even so, the current data should be interpreted with appropriate caution. They support a meaningful pathway association, but they do not by themselves demonstrate that HBV directly triggers ZBP1-dependent necroptosis in a causal or exclusive manner.

Recent advances in HBV research also help place these findings in a broader context. Studies of HBV cccDNA persistence and minichromosome biology^31,32, together with improved HBV cell culture systems^33,34, have highlighted the complexity of viral gene regulation beyond what can be captured by conventional bulk tissue readouts alone. Single-hepatocyte analyses have added further resolution by showing that viral transcription can be highly heterogeneous within the liver³⁵. At the same time, recent work on HBV-host interactions and innate immune recognition has made it increasingly clear that persistent infection is shaped by multilayered crosstalk between viral products and host defense pathways^36,37. In parallel, ongoing advances in the understanding of endogenous Z-RNA sensing and ZBP1-dependent necroptotic programs provide an updated mechanistic framework for interpreting the ZBP1-associated axis identified in the present study^38–40.

Several limitations should still be acknowledged. First, no gain- or loss-of-function experiments were performed, and causality therefore cannot yet be established. Pharmacological inhibition of necroptosis, such as Necrostatin-1 treatment, or genetic manipulation of Zbp1/Ripk3/Mlkl would be required to determine whether this axis causally contributes to liver injury. Second, only a single age window of HBV-Tg mice was examined, limiting the temporal generalizability of the findings. Third, the study was based on bulk liver RNA-seq and did not include cell-type-resolved analysis or validation in human liver samples. Nevertheless, these limitations do not diminish the value of the present work as a discovery-stage study. Rather, they define its proper interpretive scope: our data support ZBP1-associated necroptotic signaling as a biologically plausible and worthwhile candidate pathway in HBV-Tg livers, and they provide a solid basis for future mechanistic and translational studies.

Conclusions

This study provides a transcriptomic overview of HBV-Tg mouse livers and identifies candidate genes, GO terms, and pathways associated with HBV-related liver responses. Combined RNA-seq and Western blot analyses revealed increased ZBP1 expression together with elevated p-RIPK3 and p-MLKL levels in this chronic transgenic model. Beyond cataloguing candidate DEGs, the present work extends earlier HBV-Tg omics studies from apoptosis-, lipid metabolism-, and oxidative stress-centered signatures toward an interferon/ZBP1-associated necroptotic axis. These findings support the biological relevance of this pathway in HBV-Tg liver tissue and provide a useful hypothesis-generating framework for future functional validation.

Cropped blot images are shown in the main figure, and the corresponding full-length blots are provided in the Supplementary Information.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Zean Wang, Mengyuan Zhao and Yun Fu contributed equally to this work and should be considered co-first authors ZAW and MYZ conceived and designed the study. ZAW, MYZ , YF, LLL, and FG performed the animal experiments. ZAW, MYZ and YH performed histopathological examination and WB. ZAW, MYZ, MS and GLZ analyzed the data and reviewed the manuscript. All authors read and approved the final manuscript.

Funding

This study was funded by the National Natural Science Foundation of China (grant no. 81874451) and Anhui Provincial Higher Education Science Research Projects (Natural Science Category): Research on the Correlation Between Liver-Gallbladder Dysfunction and Modern Lifestyle Based on Traditional Chinese Medicine Theory (Project No. 2024AH051614).

Data availability

The data used to support the results of this study can be obtained from the National Genomics Data Center under accession number PRJCA007179 (https://ngdc.cncb.ac.cn).

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

All experimental procedures were approved by the Ethics Committee of Anhui College of Traditional Chinese Medicine and complied with the National Institutes of Health Animal Care and Welfare Guidelines (NIH Publication No. 80–23) and ARRIVE guidelines 2.0.