Pandemic influenza A (H1N1) virus causes abortive infection of primary human T cells

Investigating the effects of Laggera pterodonta on H3N2-Induced inflammatory and immune responses through network pharmacology, molecular docking, and experimental validation in a mice model

For centuries, Laggera pterodonta (LP), a Chinese herbal medicine, has been widely employed for treating respiratory infectious diseases; however, the mechanism underlying LP's effectiveness against the influenza A/Aichi/2/1968 virus (H3N2) remains elusive. This study aims to shed light on the mechanism by which LP combats influenza in H3N2-infected mice. First, we conducted quasi-targeted metabolomics analysis using liquid chromatography-mass spectrometry to identify LP components. Subsequently, network pharmacology, molecular docking, and simulation were conducted to screen candidate targets associated with AKT and NF-κB. In addition, we conducted a series of experiments including qPCR, hematoxylin-eosin staining, flow cytometry, immunohistochemistry, and enzyme-linked immunosorbent assay to provide evidence that LP treatment in H3N2-infected mice can reduce pro-inflammatory cytokine levels (TNF-α, IL-6, IL-1β, and MCP-1) while increasing T cells (CD3+, CD4+, and CD8+) and syndecan-1 and secretory IgA expression. This, in turn, aids in the prevention of excessive inflammation and the fortification of immunity, both of which are compromised by H3N2. Finally, we utilized a Western blot assay to confirm that LP indeed inhibits the AKT/NF-κB signaling cascade. Thus, the efficacy of LP serves as a cornerstone in establishing a theoretical foundation for influenza treatment.

Evaluation of disease severity and prediction of severe cases in children hospitalized with influenza A (H1N1) infection during the post-COVID-19 era: a multicenter retrospective study

BACKGROUND

The rebound of influenza A (H1N1) infection in post-COVID-19 era recently attracted enormous attention due the rapidly increased number of pediatric hospitalizations and the changed characteristics compared to classical H1N1 infection in pre-COVID-19 era. This study aimed to evaluate the clinical characteristics and severity of children hospitalized with H1N1 infection during post-COVID-19 period, and to construct a novel prediction model for severe H1N1 infection.

METHODS

A total of 757 pediatric H1N1 inpatients from nine tertiary public hospitals in Yunnan and Shanghai, China, were retrospectively included, of which 431 patients diagnosed between February 2023 and July 2023 were divided into post-COVID-19 group, while the remaining 326 patients diagnosed between November 2018 and April 2019 were divided into pre-COVID-19 group. A 1:1 propensity-score matching (PSM) was adopted to balance demographic differences between pre- and post-COVID-19 groups, and then compared the severity across these two groups based on clinical and laboratory indicators. Additionally, a subgroup analysis in the original post-COVID-19 group (without PSM) was performed to investigate the independent risk factors for severe H1N1 infection in post-COIVD-19 era. Specifically, Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to select candidate predictors, and logistic regression was used to further identify independent risk factors, thus establishing a prediction model. Receiver operating characteristic (ROC) curve and calibration curve were utilized to assess discriminative capability and accuracy of the model, while decision curve analysis (DCA) was used to determine the clinical usefulness of the model.

RESULTS

After PSM, the post-COVID-19 group showed longer fever duration, higher fever peak, more frequent cough and seizures, as well as higher levels of C-reactive protein (CRP), interleukin 6 (IL-6), IL-10, creatine kinase-MB (CK-MB) and fibrinogen, higher mechanical ventilation rate, longer length of hospital stay (LOS), as well as higher proportion of severe H1N1 infection (all P < 0.05), compared to the pre-COVID-19 group. Moreover, age, BMI, fever duration, leucocyte count, lymphocyte proportion, proportion of CD3+ T cells, tumor necrosis factor α (TNF-α), and IL-10 were confirmed to be independently associated with severe H1N1 infection in post-COVID-19 era. A prediction model integrating these above eight variables was established, and this model had good discrimination, accuracy, and clinical practicability.

CONCLUSIONS

Pediatric H1N1 infection during post-COVID-19 era showed a higher overall disease severity than the classical H1N1 infection in pre-COVID-19 period. Meanwhile, cough and seizures were more prominent in children with H1N1 infection during post-COVID-19 era. Clinicians should be aware of these changes in such patients in clinical work. Furthermore, a simple and practical prediction model was constructed and internally validated here, which showed a good performance for predicting severe H1N1 infection in post-COVID-19 era.

Bioinformatics analysis identifies a key gene HLA_DPA1 in severe influenza-associated immune infiltration

BACKGROUND

Severe influenza is a serious global health issue that leads to prolonged hospitalization and mortality on a significant scale. The pathogenesis of this infectious disease is poorly understood. Therefore, this study aimed to identify the key genes associated with severe influenza patients necessitating invasive mechanical ventilation.

METHODS

The current study utilized two publicly accessible gene expression profiles (GSE111368 and GSE21802) from the Gene Expression Omnibus database. The research focused on identifying the genes exhibiting differential expression between severe and non-severe influenza patients. We employed three machine learning algorithms, namely the Least Absolute Shrinkage and Selection Operator regression model, Random Forest, and Support Vector Machine-Recursive Feature Elimination, to detect potential key genes. The key gene was further selected based on the diagnostic performance of the target genes substantiated in the dataset GSE101702. A single-sample gene set enrichment analysis algorithm was applied to evaluate the participation of immune cell infiltration and their associations with key genes.

RESULTS

A total of 44 differentially expressed genes were recognized; among them, we focused on 10 common genes, namely PCOLCE2, HLA_DPA1, LOC653061, TDRD9, MPO, HLA_DQA1, MAOA, S100P, RAP1GAP, and CA1. To ensure the robustness of our findings, we employed overlapping LASSO regression, Random Forest, and SVM-RFE algorithms. By utilizing these algorithms, we were able to pinpoint the aforementioned 10 genes as potential biomarkers for distinguishing between both cases of influenza (severe and non-severe). However, the gene HLA_DPA1 has been recognized as a crucial factor in the pathological condition of severe influenza. Notably, the validation dataset revealed that this gene exhibited the highest area under the receiver operating characteristic curve, with a value of 0.891. The use of single-sample gene set enrichment analysis has provided valuable insights into the immune responses of patients afflicted with severe influenza that have further revealed a categorical correlation between the expression of HLA_DPA1 and lymphocytes.

CONCLUSION

The findings indicated that the HLA_DPA1 gene may play a crucial role in the immune-pathological condition of severe influenza and could serve as a promising therapeutic target for patients infected with severe influenza.

Integrated analysis of miRNA-mRNA expression of newly emerging swine H3N2 influenza virus cross-species infection with tree shrews

BACKGROUND

Cross-species transmission of zoonotic IAVs to humans is potentially widespread and lethal, posing a great threat to human health, and their cross-species transmission mechanism has attracted much attention. miRNAs have been shown to be involved in the regulation of IAVs infection and immunity, however, few studies have focused on the molecular mechanisms underlying miRNAs and mRNAs expression after IAVs cross-species infection.

METHODS

We used tree shrews, a close relative of primates, as a model and used RNA-Seq and bioinformatics tools to analyze the expression profiles of DEMs and DEGs in the nasal turbinate tissue at different time points after the newly emerged swine influenza A virus SW2783 cross-species infection with tree shrews, and miRNA-mRNA interaction maps were constructed and verified by RT-qPCR, miRNA transfection and luciferase reporter assay.

RESULTS

14 DEMs were screened based on functional analysis and interaction map, miR-760-3p, miR-449b-2, miR-30e-3p, and miR-429 were involved in the signal transduction process of replication and proliferation after infection, miR-324-3p, miR-1301-1, miR-103-1, miR-134-5p, miR-29a, miR-31, miR-16b, miR-34a, and miR-125b participate in negative feedback regulation of genes related to the immune function of the body to activate the antiviral immune response, and miR-106b-3p may be related to the cross-species infection potential of SW2783, and the expression level of these miRNAs varies in different days after infection.

CONCLUSIONS

The miRNA regulatory networks were constructed and 14 DEMs were identified, some of them can affect the replication and proliferation of viruses by regulating signal transduction, while others can play an antiviral role by regulating the immune response. It indicates that abnormal expression of miRNAs plays a crucial role in the regulation of cross-species IAVs infection, which lays a solid foundation for further exploration of the molecular regulatory mechanism of miRNAs in IAVs cross-species infection and anti-influenza virus targets.

Impact of patient characteristics on innate immune responses and inflammasome activation in ex vivo human lung tissues infected with influenza A virus

Background

Influenza A virus (IAV) infection poses a persistent global health challenge, necessitating a nuanced grasp of host immune responses for optimal interventions. While the interplay between aging, immunosenescence, and IAV is recognized as key in severe lower respiratory tract infections, the role of specific patient attributes in shaping innate immune reactions and inflammasome activity during IAV infection remains under-investigated. In this study, we utilized an ex vivo infection model of human lung tissues with H3N2 IAV to discern relationships among patient demographics, IAV nucleoprotein (NP) expression, toll-like receptor (TLR) profiles, PD-1/PD-L1 markers, and cytokine production.

Methods

Our cohort consisted of thirty adult patients who underwent video-assisted thoracoscopic surgery during 2018-2019. Post-surgical lung tissues were exposed to H3N2 IAV for ex vivo infections, and the ensuing immune responses were profiled using flow cytometry.

Results

We observed pronounced IAV activity within lung cells, as indicated by marked NP upregulation in both epithelial cells (P = 0.022) and macrophages (P = 0.003) in the IAV-exposed group relative to controls. Notably, interleukin-2 levels correlated with variations in TLR1 expression on epithelial cells and PD-L1 markers on macrophages. Age emerged as a modulating factor, dampening innate immune reactions, as evidenced by reduced interleukin-2 and interferon-γ concentrations (both adjusted P < 0.05). Intriguingly, a subset of participants with pronounced tumor necrosis factor-alpha post-mock infection (Cluster 1) showed attenuated cytokine responses in contrast to their counterparts in Cluster 2 and Cluster 3 (all adjusted P < 0.05). Individuals in Cluster 2, characterized by a low post-mock infection NP expression in macrophages, exhibited reduced variations in both NP and TLR1-3 expressions on these cells and a decreased variation in interleukin-2 secretion in comparison to their Cluster 3 counterparts, who were identified by their elevated NP macrophage expression (all adjusted P < 0.05).

Conclusion

Our work elucidates the multifaceted interplay of patient factors, innate immunity, and inflammasome responses in lung tissues subjected to ex vivo H3N2 IAV exposure, reflecting real-world lower respiratory tract infections. While these findings provide a foundation for tailored therapeutic strategies, supplementary studies are requisite for thorough validation and refinement.

The Enigma of Lymphocyte Apoptosis in the Response to Influenza Virus Infection

In the pathogenesis of influenza virus infection, lymphocyte apoptosis as a part of the infection and/or the immune response to the virus can be somewhat puzzling. The percentage of human T lymphocytes within the peripheral blood mononuclear cell population that becomes apoptotic greatly exceeds the percentage that are infected after exposure to the virus, consistent with substantial apoptosis of bystander T lymphocytes. Studies reveal an important role of viral neuraminidase expression by co-cultured monocyte/macrophages in induction of apoptosis, including that of uninfected bystander lymphocytes. Despite these observations, it is a reasonable perspective to recognize that the development of lymphocyte apoptosis during the response to infection does not preclude a successful immune response and recovery of the infected host in the great majority of cases. Further investigation is clearly warranted to understand its role in the pathogenesis of influenza virus infection for human subjects.

Co-expression network analysis identifies potential candidate hub genes in severe influenza patients needing invasive mechanical ventilation

BACKGROUND

Influenza is a contagious disease that affects people of all ages and is linked to considerable mortality during epidemics and occasional outbreaks. Moreover, effective immunological biomarkers are needed for elucidating aetiology and preventing and treating severe influenza. Herein, we aimed to evaluate the key genes linked with the disease severity in influenza patients needing invasive mechanical ventilation (IMV). Three gene microarray data sets (GSE101702, GSE21802, and GSE111368) from blood samples of influenza patients were made available by the Gene Expression Omnibus (GEO) database. The GSE101702 and GSE21802 data sets were combined to create the training set. Hub indicators for IMV patients with severe influenza were determined using differential expression analysis and Weighted correlation network analysis (WGCNA) from the training set. The receiver operating characteristic curve (ROC) was also used to evaluate the hub genes from the test set's diagnostic accuracy. Different immune cells' infiltration levels in the expression profile and their correlation with hub gene markers were examined using single-sample gene set enrichment analysis (ssGSEA).

RESULTS

In the present study, we evaluated a total of 447 differential genes. WGCNA identified eight co-expression modules, with the red module having the strongest correlation with IMV patients. Differential genes were combined to obtain 3 hub genes (HLA-DPA1, HLA-DRB3, and CECR1). The identified genes were investigated as potential indicators for patients with severe influenza who required IMV using the least absolute shrinkage and selection operator (LASSO) approach. The ROC showed the diagnostic value of the three hub genes in determining the severity of influenza. Using ssGSEA, it has been revealed that the expression of key genes was negatively correlated with neutrophil activation and positively associated with adaptive cellular immune response.

CONCLUSION

We evaluated three novel hub genes that could be linked to the immunopathological mechanism of severe influenza patients who require IMV treatment and could be used as potential biomarkers for severe influenza prevention and treatment.