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Influenza A virus infection activates TLR3-mediated necroptosis
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Weijie Lia, Congying Huanga, Ziling Zenga, Xiang Lia, Jia Xub, Tian Gonga, c, Hao Zhangb, Xinyan Zhangd, Ping Wanga, Yuanjia Hue, Haiyu Xua, f, *, Lijuan Songa, *
Science of Traditional Chinese Medicine | 2026, 4(1) : 40 - 49
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Science of Traditional Chinese Medicine | 2026, 4(1): 40-49
• Original Research •
Influenza A virus infection activates TLR3-mediated necroptosis
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Weijie Lia, Congying Huanga, Ziling Zenga, Xiang Lia, Jia Xub, Tian Gonga, c, Hao Zhangb, Xinyan Zhangd, Ping Wanga, Yuanjia Hue, Haiyu Xua, f, *, Lijuan Songa, *
Affiliations
  • aState Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
  • bGuang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
  • cSchool of Traditional Chinese Materia Medica, Shenyang Pharmaceutical University, Shenyang, China
  • dNational Institutes for Food and Drug Control, Beijing, China
  • eState Key Laboratory of Mechanism and Quality of Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao SAR, China
  • fLiaoning University of Traditional Chinese Medicine, Dalian, China
Published: 2026-03-25 doi: 10.1097/st9.0000000000000093
Outline
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Background:

Influenza A virus (IAV) is a negative-sense RNA virus of the Orthomyxoviridae family and is the etiological agent of a highly contagious acute respiratory disease that can lead to acute lung injury.

Objective:

To elucidate the molecular mechanisms of IAV infection, an integrative research approach combining gene expression profiling, multinetwork analysis, and in vivo experimental validations was employed.

Methods:

First, a series of network-based analyses were performed, including protein-protein interaction network construction, weighted gene co-expression network analysis, and subsequent gene set enrichment analysis, to identify the major underlying mechanisms of IAV infection. Following gene expression analysis, core targets, both direct and indirect regulators, were screened. An IAV (H1N1) strain A/PR/8/34-induced acute lung injury mouse model was constructed for in vivo validations. Batch one included two groups to evaluate findings from the multi-network analysis: Mock (n = 10; 5 males and 5 females) and IAV (n = 10; 5 males and 5 females). Batch two included three groups to assess the role of toll-like receptor 3 (TLR3) in IAV infection: Mock (n = 6; 3 males and 3 females), IAV (n = 6; 3 males and 3 females), and TLR3 inhibitor (n = 6; 3 males and 3 females). Body weight was measured on days 0, 3, and 5 after infection. On day 5, lung tissues were collected to assess viral load and histopathological changes. Key targets were examined using enzyme-linked immunosorbent assay, Western blotting, and immunofluorescence staining, both in sera and lung tissues.

Results:

IAV infection was significantly associated with dysregulation of the immune-inflammation system, such as the LTR, nucle-otide-binding oligomerization domain-(NOD) like receptor, retinoic acid-inducible gene I-like receptor, and nuclear factor kappa-B signaling pathways. Gene set enrichment analysis further indicated that the TLR and necroptosis signaling pathways played crucial roles in the progression of IAV infection (TLR signaling pathway normalized enrichment score = 2.3941, P = 1.00 × 10−10; necroptosis normalized enrichment score = 1.9421, P = 6.21 × 10−7). Among the core targets, TLR3 and mixed lineage kinase domain-like protein (MLKL) may regulate gene expression at the transcriptional level (all P < 0.05). In vivo validation using an IAV (PR8) infected acute lung injury mouse model demonstrated increased viral load and lung index, alveolar structural damage, and inflammatory cell infiltration. Immunofluorescence staining exhibited large gaps in Lamin B1 staining and breaches in Emerin signals following IAV-PR8 infection. Expression levels of TLR3, p-receptor-interacting serine/threonine-protein kinase 3 (RIPK3)/RIPK3, and p-mixed lineage kinase domain-like protein (MLKL)/MLKL proteins in lung tissues, as well as proinflammatory factors and mediators in sera, were significantly elevated after IAV infection. Moreover, enhanced neutrophil infiltration (myeloperoxidase) and citrullinated histone H3 (a neutrophil extracellular trap-specific marker), both established indicators of neutrophil extracellular trap formation, were observed. Notably, treatment with a TLR3 inhibitor significantly ameliorated IAV-induced acute lung injury by regulating necroptosis-related targets.

Conclusion:

Our study provides network-based in vivo evidence that TLR3-receptor-interacting serine/threonine-protein kinase 3-MLKL-mediated necroptosis may underlie IAV-induced acute lung injury and could serve as a potential therapeutic target in severe influenza cases.

Influenza A virus  /  Necroptosis  /  "Immune-inflammation" regulation  /  TLR3-RIPK3-MLKL signaling  /  Network analyses
Weijie Li, Congying Huang, Ziling Zeng, Xiang Li, Jia Xu, Tian Gong, Hao Zhang, Xinyan Zhang, Ping Wang, Yuanjia Hu, Haiyu Xu, Lijuan Song. Influenza A virus infection activates TLR3-mediated necroptosis[J]. Science of Traditional Chinese Medicine, 2026 , 4 (1) : 40 -49 . DOI: 10.1097/st9.0000000000000093
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1.　Introduction
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Seasonal human influenza viruses cause acute respiratory infections that affect the entire population, resulting in approximately 650,000 deaths annually worldwide and imposing a significant public health and economic burden.[1] Influenza A virus (IAV), a negative-sense RNA virus of the Orthomyxoviridae family, is the etiological agent of this highly contagious acute respiratory disease.[2,3] Influenza outbreaks lead to substantial morbidity and mortality each year, particularly from pneumonia in the elderly.[4-6] Vaccination is currently considered the most effective strategy for preventing or mitigating the disease caused by cocirculating influenza A and B viruses.[7] However, seasonal influenza vaccines primarily utilize inactivated viruses, and the virus's ability to evade host antiviral immune responses necessitates annual strain updates.[8] Therefore, elucidating the pathogenic mechanisms of IAV infection is essential for the development of more effective vaccines and novel antiviral therapies.
Recent studies have indicated that IAV activates multiple programmed cell death pathways, including apoptosis, necrosis, necroptosis, and pyroptosis, which play essential roles in host defense against viral infection and replication.[9] The elimination of infected cells through programmed cell death is a well-established mechanism for limiting viral spread. Among these pathways, necroptosis is a form of caspase-independent necrotic cell death triggered by bacterial and viral infections and is mainly regulated by receptor-interacting serine/threonine-protein kinase 3 (RIPK3), receptor-interacting serine/threonine-protein kinase 1 (RIPK1), and mixed lineage kinase domain-like protein (MLKL).[10,11] Both RIPK1 and RIPK3 contain an N-terminal kinase domain followed by a RIP homotypic interaction motif.[12] Physical interactions among RIPK1, RIPK3, and MLKL within the necrosome complex induce MLKL phosphorylation, leading to its oligomerization and execution of necroptotic cell death.[13,14]
Clinical practice has demonstrated that traditional Chinese medicine (TCM) holds unique advantages in treating influenza virus infection, as it exerts both direct antiviral effects and modulates immune-inflammatory imbalances. Elucidating the key molecular mechanisms underlying the progression of viral infection is crucial for developing precise therapeutic strategies. This study aims to advance the mechanistic research on TCM against the influenza virus and to provide actionable drug targets for screening natural products. However, the critical role of necroptosis in influenza pathogenesis, as well as the underlying mechanisms of IAV-mediated necroptosis, remains unclear. In this study, high-quality transcriptome data were used to construct protein-protein interaction (PPI) network and perform enrichment analyses to identify hub genes. Furthermore, weighted gene co-expression network analysis and gene set enrichment analysis (GSEA) were applied to uncover potential key regulatory targets involved in disease pathogenesis. Experimental validation was then conducted using an IAV-PR8-induced acute lung injury mouse model. Collectively, our study provides new insights into the pathogenesis of IAV infection and may facilitate the development of natural product screening.
2.　Materials and methods
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2.1.　 Data mining and analysis
Transcriptome data were downloaded from the Gene Expression Omnibus (GEO) database (GEO accession No. GSE163959).[15] The dataset comprises lung tissues from 5 individuals that were either IAV- or mock-infected and subsequently analyzed for transcriptome profiling, with a focus on innate immune responses. IAV-related genes, defined as differentially expressed genes (DEGs) between the IAV-infected and mock-infected groups, were identified using the criteria of t test P < 0.05 and |fold change| > 1.
2.2.　 Network construction and analysis
The IAV-related gene interaction network was constructed and analyzed based on the interactions among DEGs between the IAV-infected and mock-infected groups to identify key IAV-related genes. First, PPIs were established using the search tool for recurring instances of neighbouring genes (STRING) database (version 12.0, http://string-db.org/), with a combined score >0.7,[16] and the interaction network was visualized using Cytoscape (Institute for Systems Biology, Seattle, Washington, USA, version 3.10.2, https://cytoscape.org/).[17,18] Key nodes were identified based on network topology parameters exceeding the median, including maximal clique centrality, degree, closeness, and betweenness.[19,20] Kyoto Encyclopedia of Genes and Genomes (updated 2024-09-01, https://www.kegg.jp/) functional enrichment analysis was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID, updated 2024-07-05, https://david.ncifcrf.gov/) with a significance threshold of P < 0.05. For gene expression analysis, GSEA was conducted and visualized using R (R Foundation for Statistical Computing, Vienna, Austria) packages including "org.Hs.eg.db," "clusterProfiler," and "pathview." Subsequently, co-expression network analysis was performed using weighted gene co-expression network analysis with the R package "corrplot" applying thresholds of P < 0.05 and Pearson correlation coefficient >0.8.
2.3.　 Virus preparation
IAV (H1N1) strain A/PR/8/34 was obtained from the American Type Culture Collection (ATCC, VR-95 Manassas, Virginia, USA), propagated in chicken embryos, and stored at −80 °C. Viral titers were determined using the chicken hemagglutination assay, and the median lethal dose (LD50) was calculated.[21]
2.4.　 Establishment of acute lung injury model
For all infections, mice were lightly anesthetized with isoflurane and administered PR8 virus intranasally in 35 μL of normal saline. Mice received 5 LD50 of the virus via intranasal drops. Body weights were measured on days 0, 3, and 5 postinfection (dpi). On day 5, mice were euthanized in accordance with institutional guidelines, and samples were collected for subsequent analyses.
2.5.　 Animal experiments
A total of 38 C57BL/6JNifdc male and female mice (5-weekold, [14.00 ± 1.00] g) were obtained from the National Institutes for Food and Drug Control. The study was approved by the Institutional Animal Care and Use Committee of the National Institutes for Food and Drug Control, Beijing, China [Approval No. 2023(B) 063]. All mice were randomly assigned to experimental groups based on body weight to ensure uniform infection efficiency. Data from 2 batches of animals were pooled for the final analysis of each experiment. Batch 1 included 2 groups to evaluate findings from the multinetwork analysis: Mock (n = 10; 5 males and 5 females) and IAV (n = 10; 5 males and 5 females). Batch 2 included 3 groups to assess the role of toll-like receptor 3 (TLR3) in IAV infection: Mock (n = 6; 3 males and 3 females), IAV (n = 6; 3 males and 3 females), and TLR3 inhibitor (n = 6; 3 males and 3 females). Baseline health status and body weight were assessed, and characteristics were generally consistent across groups. All animals were housed in an animal biosafety level 2 (ABSL-2) containment facility at (24 ± 1) °C with a 12-hour light/dark cycle and had ad libitum access to standard rodent chow and water.
The TLR3 inhibitor ODN 24991 sodium (MedChemExpress, Monmouth Junction, New Jersey, USA, HY-150746A) was dissolved in saline and administered at a dose of 1 mg/kg via intraperitoneal injection 1 hour before IAV infection. All animals in each group were used for serum factor analysis. Whole lung tissue samples were equally divided for histopathological evaluation and protein- or RNA-based molecular analyses.
2.6.　 Lung index and histology
The lung index was calculated as the ratio of lung weight to body weight. Hematoxylin-eosin (H&E) staining (Pinuofei Biotechnology, Wuhan, China, S191003) was performed to assess lung injury. Briefly, lung tissues were fixed in 4% paraformaldehyde for 24 hours, dehydrated through a graded alcohol series, cleared with xylene, and embedded in paraffin. The paraffin blocks were mounted on a microtome and sectioned at 4 to 6 μm. Sections were stained with H&E, and pathological changes in lung tissue across different groups were evaluated by 2 trained observers blinded to the experimental groups. Each slide was scored based on the severity of histologic changes, including inflammation, interstitial pneumonia, edema, alveolitis, bronchiolitis, alveolar destruction, mononuclear cell infiltration, pulmonary hemorrhage, and peribronchiolar inflammation.[22] The scoring system was as follows: no pathological changes = 0; mild = 1; moderate = 2; severe = 3; extremely severe = 4. The acute lung injury model was confirmed by H&E staining and lung injury scoring.
2.7.　 RNA isolation and real-time quantitative polymerase chain reaction (PCR)
Total RNA was extracted from lung tissue using TRIzol (Tiangen Biochemical Technology Co., Ltd., Beijing, China, A1212A01). RNA samples were reverse-transcribed into cDNA using FastKing gDNA Dispelling RT SuperMix (Tiangen Biochemical Technology Co., Ltd., Beijing, China, B0516A). Real-time quantitative PCR was performed using Taq Pro Universal SYBR qPCR Master Mix (Vazyme Biotech Co., Ltd., Nanjing, China, 7E1081B5). The reaction conditions were as follows: 95 °C for 30 seconds, followed by 40 cycles of 95 °C for 10 seconds and 60 °C for 30 seconds, in a total volume of 20 µL. Primer sequences are listed in Supplemental Table S1, https://links.lww.com/STCM/A69. Relative mRNA expression was normalized to β-actin and calculated using the 2-ΔΔCt method.
2.8.　 Enzyme-linked immunosorbent assay (ELISA)
At 5 dpi, blood was collected and allowed to clot at room temperature for 2 hours, followed by centrifugation at 12,000 r/min at 4°C for 15 minutes to collect the supernatant. Serum concentrations of cytokines, chemokines, and interferons (IFNs) were measured using ELISA kits according to the manufacturers' instructions. All ELISA kits were purchased from Shanghai Enzyme-linked Biotechnology Co. (Shanghai, China) and included mouse C-C motif chemokine ligand (CCL)-2, CCL-5, CCL-10, interleukin (IL)-18, IL-6, tumor necrosis factor-alpha (TNF-α), IFN-α, IFN-β, and IFN-γ (Supplemental Table S2, https://links.lww.com/STCM/A69). Additionally, serum levels of IFN-γ, CCL-5, IL-6, and TNF-α were measured using the ABplex Mouse 4-Plex Custom Panel (ABclonal, Wuhan, China, RK04381).
2.9.　 Western blotting analysis
Following radioimmunoprecipitation assay (RIPA) lysis, total protein was extracted and quantified using the bicinchoninic acid (BCA) method (Beyotime Biotechnology Co., Ltd., Shanghai, China, P0012). Western blotting analysis was performed according to protocols described in our previous studies.[23-26] Antibodies against TLR3 (Affinity Biosciences, Changzhou, China, DF6415, 1:1000 dilution), p-MLKL (ABclonal Technology, Wuhan, China, AP0949, 1:1000 dilution), MLKL (Proteintech, Wuhan, China, 66675-1-Ig, 1:1000 dilution), p-RIPK3 (ABclonal Technology, Wuhan, China, AP1260, 1:1000 dilution), and RIPK3 (ABclonal Technology, Wuhan, China, A5431, 1:1000 dilution) were used as listed in Supplemental Table S3, https://links.lww.com/STCM/A69. Beta-actin (rabbit Anti-β-actin, Bioss Biotechnology, Beijing, China, bs-0061R, 1:5000 dilution) served as a loading control for lung tissue samples.
2.10.　 Immunofluorescence staining
Double and triple immunofluorescence staining was performed as previously described.[27] Primary antibodies to rabbit Emerin and mouse Lamin B1 were used for Emerin/Lamin B1 double staining (Proteintech, Wuhan, China; 66095-1-1g and 10351-1-AP; 1:200 dilution). Primary antibodies to rabbit citrullinated histone H3 (Cit-H3), mouse myeloperoxidase (MPO), and rabbit eukaryotic nucleoprotein (NP) were used for Cit-H3/MPO/NP triple staining (Proteintech, Wuhan, China; 66177-1-1g, 13754-1-AP, and 18009-1-AP; 1:200, 1:400, and 1:200 dilution). Sections were incubated with FITC- (488 nm), CY3-(555 nm), and CY5- (647 nm) conjugated secondary antibodies (SeraCare, Beijing, China; 5220-0336 and 5220-0341; 1:400 dilution) for 2 hours at room temperature, protected from light. Nuclei were counterstained with 4',6-diamidino-2-phenylindole (Beyotime Biotechnology, Shanghai, China, C1006) for 4 minutes at room temperature. Stained sections were visualized and imaged using a confocal microscope (Abberior, Göttingen, Germany) and Pannoramic Scan II (3DHISTECH, Budapest, Hungary). All antibodies used are listed in Supplemental Table S3, https://links.lww.com/STCM/A69. Reagents and instruments involved in the experiment are listed in Supplemental Tables S4 and S5, https://links.lww.com/STCM/A69.
2.11.　 Statistical analyses
Statistical analyses were performed using GraphPad Prism 8.0 software (San Diego, California, USA). Data are expressed as the mean ± standard deviation and were analyzed using analysis of variance with Sidak multiple comparisons test for comparisons across multiple groups, and an unpaired t test for comparisons between 2 groups. Differences were considered statistically significant when the P value was less than 0.05.
3.　Results
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3.1.　 The imbalance of the "immune-inflammation" system is closely related to the pathogenesis of IAV infection
To identify candidate targets underlying the pathogenesis of IAV infection, data mining was performed on transcriptome data from the GEO database (GEO No. GSE163959). A total of 1113 IAV-related genes were identified in lung tissues, including 707 upregulated genes and 406 downregulated genes, based on the criteria of a t test P < 0.05 and |fold change| > 1.
The heatmap of DEGs is shown in Figure 1A, suggesting a clear separation between the IAV-infected and mock-infected groups. A PPI network of IAV-related genes was then constructed based on gene interactions (combined score > 0.7) as described in our previous studies.[19,20] The network consisted of 283 nodes and 1691 interactions. Based on network topology parameters, including "degree," "closeness," and "betweenness," 93 hub genes were identified (median values: degree = 4.0000, closeness = 85.2524, betweenness = 35.4127). These hub genes were involved in multiple biological pathways, including immune-inflammation regulation, signal transduction, and cellular processes (Fig. 1B). Notably, IAV infection upregulated multiple biological pathways, particularly canonical immune and inflammatory pathways, which are closely associated with the progression of IAV infection.[28] Subsequently, a co-expression network was constructed using the links of the gene expression across tissues, comprising 826 nodes and 32,907 significant co-expression correlations (P < 0.05, Pearson correlation coefficient >0.8). Using the degree parameter, 412 hub genes were screened (median degree = 22). Overall, 82 overlapping genes between the PPI and coexpression networks were selected as candidate genes for further bioinformatics analysis (Fig. 1C).
Next, a subnetwork of "IAV-infection-related genes-pathway" among DEGs was constructed, suggesting that the molecular mechanisms of IAV infection may involve the imbalance of the "immune-inflammation" system via TLR and necroptosis signaling pathways. Candidate genes were selected based on their topological importance, such as TNF-α, IL-6, CCL-5, and TLR3 (Fig. 1D). A 3-layer circular network of the intersection genes showed that IAV infection was strongly associated with dysregulation of the "immune-inflammation" system, such as TLR, NOD-like receptor, retinoic acid-inducible gene I-like receptor (RLR), and C-type lectin receptor signaling pathways, cellular processes (necroptosis), and signal transduction pathways, including Janus kinase-signal transducer and activator of transcription (JAK-STAT) (Fig. 1E, 1F). Among these, the TLR signaling pathway and necroptosis occupied central positions within the network cluster. GSEA was performed using P values and normalized enrichment scores (NES) to assess statistical significance (Fig. 1G), corroborating the functional enrichment results (TLR signaling pathway: NES = 2.3941, P = 1.00 × 10−10; necroptosis: NES = 1.9421,P = 6.21 × 10−7), demonstrating activation of TLR and necroptosis pathways following IAV challenge. Figure 1H showed that TLR3 and MLKL may regulate the expression of other genes at the transcriptional level (all P < 0.05, Pearson correlation coefficient >0.8). Innate immune responses to IAV infection are partially initiated by TLR3, [29] suggesting that TLR3 activation could act upstream of necroptosis in IAV pathogenesis. Moreover, excessive TLR expression is known to trigger an active inflammatory response and activate the downstream nuclear factor kappa-B signaling pathway. Based on these findings, we hypothesize that TLR3-mediated necroptosis signaling represents a key molecular mechanism in the pathogenesis of influenza.
3.2.　 IAV triggers TLR3-mediated necroptosis via regulating RIPK3/MLKL signaling
To validate the findings from the bioinformatics analyses, in vivo experiments were performed using a mouse model of acute lung injury induced by IAV (H1N1) strain A/PR/8/34 (PR8) (Fig. 2A). The average body weight of mice reached its lowest point on day 5 after infection (P < 0.001, Fig. 2B), after which the mice were euthanized, and viral load in lung tissues was assessed by viral RNA real-time quantitative PCR. As shown in Fig. 2C and 2D, both viral load and lung indexes were significantly increased in IAV-infected mice compared with the mock-infected group (all P< 0.001). Lung histopathology was evaluated using H&E staining, and semiquantitative analysis was performed to determine the degree of histologic changes.[22] In the mock-infected group, lung tissue exhibited normal alveolar cavities, thin alveolar walls, and no edema or hemorrhage. In contrast, IAV infection induced alveolar structural damage, thickened alveolar walls, pulmonary congestion, and inflammatory cell infiltration (Fig. 2E). Semiquantitative scoring confirmed the occurrence of acute lung injury, consistent with the histopathological observation (P < 0.001, Fig. 2F).
Our network-based analyses suggested that TLR3-mediated necroptosis signaling may underlie the pathogenesis of influenza. Lamins are important caspase substrates and key components of the nuclear lamina,[30,31] and loss of nuclear Lamin B1 disrupts the integrity of the nuclear envelope.[32] To examine nuclear membrane morphology, lamin cleavage, and structural integrity in lung tissues of IAV-PR8-infected mice, immunofluo-rescence microscopy was performed.[33] Imaging of the nuclear lamina protein Lamin B1 and the inner nuclear membrane protein Emerin revealed ovoid nuclei with well-defined boundaries in mock-infected mice. In contrast, IAV-PR8 infection induced large gaps in Lamin B1 staining and breaches in the Emerin signal (Fig. 3A). Consistently, the fluorescence intensity detected from each emission channel was significantly decreased (all P < 0.01, Fig. 3B), indicating reduced Lamin B1 and Emerin protein levels.
To our knowledge, MLKL is a critical substrate of RIPK3, and phosphorylation of MLKL (p-MLKL) represents a key event in necroptosis. [34-36] We examined key necroptosis mediators and downstream effector molecules in lung tissues following viral infection (Fig. 3C). Consistent with previous observation, TLR3 protein expression in lung tissues was significantly increased after IAV-PR8 infection (P < 0.01, Fig. 3D). Additionally, the protein levels of key necroptosis components, including p-MLKL/MLKL and p-RIPK3/RIPK3, were markedly elevated in IAV-PR8-infected mice (P < 0.05 and P < 0.01, respectively; Fig. 3E, 3F). These findings indicated that TLR3 may mediate activation of RIPK3/MLKL, subsequently contributing to nuclear envelope damage during IAV infection.
3.3.　 IAV infection drives neutrophil recruitment and induces inflammatory response
To investigate the role of TLR3-mediated necroptosis in IAV-induced inflammatory responses in vivo, proinflammatory factors and mediators were measured using ELISA. Notably, IFN-α, IFN-β, and IFN-γ protein levels were significantly elevated (P <0.001, P < 0.01, and P < 0.001, respectively; Fig. 4A). Type I interferons, including IFN-α and IFN-β, induce the expression of interferon-stimulated genes that confer antiviral activity to host cells. However, aberrant activation can exacerbate inflammation and autoimmunity.[37] Cytokines and chemokines play critical roles in shaping adaptive immunity during viral infection.[38,39] Consistently, the serum levels of chemokines were markedly upregulated in IAVPR8-infected mice, including CCL-2, CCL-5, and CCL-10 (P < 0.05, P < 0.05, and P < 0.001, respectively; Fig. 4B). Furthermore, key inflammatory cytokines associated with necroptosis (IL-18, IL-6, and TNF-α) were also elevated in the sera of IAV-infected mice (P < 0.01, P < 0.05, and P < 0.001, respectively; Fig. 4C).
Notably, IAV infection induces neutrophil infiltration, a hallmark of IAV-induced pathology,[40,41] and stimulates neutrophil extracellular trap (NET) formation by human neutrophils in vitro via a distinctive pathway.[42] To determine whether these pathological changes were associated with neutrophil infiltration and NET formation, immunofluorescence staining was performed to assess neutrophil infiltration (MPO, a known NET marker) and the expression of Cit-H3 (a NET-specific marker).[43,44] Consistent with these findings, IAV-PR8 infection significantly increased neutrophil infiltration (MPO, P < 0.05) and NET formation (Cit-H3, P < 0.001) in lung tissues (Fig. 4D,4E). These results indicate that IAV infection promotes neutrophil infiltration and NET formation, contributing to subsequent inflammatory responses in both innate and adaptive immunity.
3.4.　 TLR3 plays a key role in activating necroptosis signaling during IAV infection
To determine whether TLR3 directly mediates necroptosis, the TLR3 inhibitor ODN 24991 sodium was administered during IAV infection. Treatment with the TLR3 inhibitor significantly attenuated body weight loss (P < 0.01, Fig. 5A). In IAV-infected mice, severe alveolar septal thickening, consolidation, pulmonary edema, and infiltration of inflammatory immune cells were observed. These pathological changes were significantly alleviated by the TLR3 inhibitor (P < 0.001, Fig. 5B, 5C). In terms of inflammation, TLR3 inhibition markedly reduced serum levels of IFN-γ, CCL-5, IL-6, and TNF-α (P < 0.001, P < 0.05, P < 0.001, and P < 0.05, respectively; Fig. 5D-5G). Moreover, the large gaps in Lamin B1 staining and breaches in the Emerin signal induced by IAV were ameliorated following TLR3 inhibition (P < 0.05 and P < 0.001, respectively; Fig. 5H, 5I). IAV infection increased NP levels and promoted NET formation, as evidenced by elevated MPO and Cit-H3 in lung tissues, which were reversed by TLR3 inhibitor treatment (all P < 0.001; Fig. 5J, 5K). Western blotting analysis revealed that the elevated protein levels of TLR3, p-MLKL/MLKL, and p-RIPK3/RIPK3 were significantly downregulated in the TLR3 inhibitor group (P < 0.01, P < 0.05, and P < 0.05, respectively; Fig. 5L, 5M).
4.　Discussion
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Accumulating evidence shows that IAV infection causes necroptosis in both infiltrating immune cells and lung structural alveolar epithelial cells.[2,45] The current study, an integrative approach combining gene expression profiling, multinetwork analysis, GSEA, and experimental validations, identified TLR3-mediated RIPK3-MLKL signaling-induced necroptosis as a key underlying molecular mechanism for regulating the imbalance network of the "immune-inflammation" network during IAV infection. Clinical studies and mouse models have demonstrated that severe H1N1 infection is associated with rapidly progressive pneumonia, extensive and diffuse alveolar damage, acute respiratory distress syndrome, and, in some cases, multiorgan failure, contributing to high mortality.[46,47] IAV can directly infect and damage lung epithelial cells and alveolar macrophages, triggering immune responses and acute lung injury, which result in pulmonary edema and impaired alveolar fluid clearance. Our data indicate that excessive immune response-induced immunopathological damage plays a critical role in the pathogenesis of acute lung injury.
Double-stranded RNAs are produced in virus-infected cells during replication and play a crucial role in eliciting antiviral responses by binding to TLR3, a member of the pattern recognition receptor family, and RLRs.[48,49] Combined treatment with sirolimus and oseltamivir attenuates H1N1-induced severe lung injury, which correlates with suppression of the mTOR-NLRP3-IL-1β axis and a reduced viral titer.[47] TLR3 and RLRs activate multiple signaling pathways, including nuclear factor kappa-B and mitogen-activated protein kinase signaling, leading to the production of proinflammatory cytokines and induction of interferon-stimulated genes through transcriptional regulation. IAV induces cytokine storm via IFN regulation, resulting in tissue damage and ultimately lung injury.[50] Concurrently, double-stranded RNA is also produced in stressed, apoptotic, or necrotic cells.[51,52] Our research showed increased expression of TLR3 protein in IAV-infected lung tissues, which subsequently induces proinflammatory cytokines, such as IFNs and ILs, contributing to the hyperin flammatory response. Programmed cell death, particularly necroptosis, participates in the pathogenesis of several diseases, including influenza.[53,54] Necroptosis is characterized by RIPK3 phosphorylation and the RIPK3-mediated phosphorylation of MLKL.[55-57] While programmed cell death is an effective mechanism for IAV clearance, eliminating infected cells, limiting viral spread, and activating adaptive immunity, necroptotic cell debris is highly immunogenic and promotes neutrophil recruitment and activation. Neutrophils, in turn, can trigger a pathogenic feed-forward inflammatory response during IAV infection, as the nucleus contains numerous damage-associated molecular patterns, including DNA.[2] Our data implicate MLKL-mediated necroptosis as a driver of IAV virulence. Importantly, the TLR3 inhibitor ameliorated IAV-induced acute lung injury by suppressing MLKL-mediated necroptosis. These findings identify TLR3-RIPK3-MLKL-mediated necroptosis as a potential therapeutic target in severe influenza.
While the use of specific pharmacological inhibitors provides strong functional evidence supporting the proposed pathway, future studies employing cell-specific conditional knockout models will be valuable to validate these findings and rule out potential off-target effects of the compounds used. This study has several limitations: (1) the transcriptome data were obtained from the GEO database, and we did not perform a comprehensive transcriptome analysis of mouse lungs infected with the IAV-PR8; (2) the therapeutic potential of targeting TLR3-RIPK3-MLKL-mediated necroptosis, including candidate drugs or signal interventions, requires further investigation.
5.　Conclusions
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In this study, we provide both network-based and in vivo evidence that TLR3-RIPK3-MLKL-mediated necroptosis may be an underlying molecular mechanism of IAV infection-induced acute lung injury. These findings offer new insights into the pathogenesis of IAV infection and may promote the research on the mechanism of TCM against influenza virus and provide actionable drug targets for natural product screening.
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doi: 10.1097/st9.0000000000000093
  • Receive Date：2025-07-21
  • Online Date：2026-06-25
  • Published：2026-03-25
Article Data
Affiliations
History
  • Received：2025-07-21
  • Accepted：2025-09-09
Affiliations
    aState Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
    bGuang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
    cSchool of Traditional Chinese Materia Medica, Shenyang Pharmaceutical University, Shenyang, China
    dNational Institutes for Food and Drug Control, Beijing, China
    eState Key Laboratory of Mechanism and Quality of Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao SAR, China
    fLiaoning University of Traditional Chinese Medicine, Dalian, China

Corresponding:

* Haiyu Xu, State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, No. 16, Nanxiaojie, Dongzhimennei, Beijing 100700, China. E-mail address: (H. Xu).
Lijuan Song, State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, No. 16, Nanxiaojie, Dongzhimennei, Beijing 100700, China. E-mail address: (L. Song).
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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