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ObjectiveTherapeutic strategies silencing and reducing the hepatitis B virus (HBV) reservoir, the covalently closed circular DNA (cccDNA), have the potential to cure chronic HBV infection. We aimed to investigate the impact of small interferring RNA (siRNA) targeting all HBV transcripts or pegylated interferon-α (peg-IFNα) on the viral regulatory HBx protein and the structural maintenance of chromosome 5/6 complex (SMC5/6), a host factor suppressing cccDNA transcription. In particular, we assessed whether interventions lowering HBV transcripts can achieve and maintain silencing of cccDNA transcription in vivo.DesignHBV-infected human liver chimeric mice were treated with siRNA or peg-IFNα. Virological and host changes were analysed at the end of treatment and during the rebound phase by qualitative PCR, ELISA, immunoblotting and chromatin immunoprecipitation. RNA in situ hybridisation was combined with immunofluorescence to detect SMC6 and HBV RNAs at single cell level. The entry inhibitor myrcludex-B was used during the rebound phase to avoid new infection events.ResultsBoth siRNA and peg-IFNα strongly reduced all HBV markers, including HBx levels, thus enabling the reappearance of SMC5/6 in hepatocytes that achieved HBV-RNA negativisation and SMC5/6 association with the cccDNA. Only IFN reduced cccDNA loads and enhanced IFN-stimulated genes. However, the antiviral effects did not persist off treatment and SMC5/6 was again degraded. Remarkably, the blockade of viral entry that started at the end of treatment hindered renewed degradation of SMC5/6.ConclusionThese results reveal that therapeutics abrogating all HBV transcripts including HBx promote epigenetic suppression of the HBV minichromosome, whereas strategies protecting the human hepatocytes from reinfection are needed to maintain cccDNA silencing.

Pegbing (peginterferon alpha‐2b) is a polyethylene glycol‐modified interferon α‐2b injection that has demonstrated favorable efficacy and safety profiles in the treatment of chronic hepatitis B (CHB). This study aimed to develop a population pharmacokinetic (PopPK) model of Pegbing in both healthy subjects and CHB patients and to investigate the influence of covariates on its pharmacokinetic behavior. Pharmacokinetic data were obtained from a Phase I trial in healthy volunteers and a Phase II trial in CHB patients. A one‐compartment model with a target‐mediated drug disposition (TMDD) component incorporating IFN receptor downregulation was established to describe the pooled data from 28 healthy subjects and 39 CHB patients. Physiologically reasonable parameters were estimated, providing a good description and prediction of the model. Furthermore, the final PopPK model was externally validated using an independent dataset of 115 CHB patients. In the covariate analysis, health status (healthy v.s. CHB) was a significant covariate, affecting the Pegbing absorption rate, creatinine clearance was associated with clearance, and body weight affected the volume of distribution. Compared with healthy subjects, CHB patients exhibited a consistent area under the curve (AUC) but a higher Cmax. A PopPK model of Pegbing in both healthy volunteers and CHB patients was successfully established. Based on the model simulation, covariate‐based dose adjustment is unnecessary for CHB patients with normal renal function.

Triggering or enhancing antitumor activity of tumor-associated macrophages is an attractive strategy for cancer treatment. We have previously shown that the cytokine interferon-γ (IFN-γ), a type II IFN, could synergize with toll-like receptor (TLR) agonists for induction of antitumor M1 macrophages. However, the toxicity of IFN-γ limits its clinical use. Here, we investigated whether the less toxic type I IFNs, IFN-α, and IFN-β, could potentially replace IFN-γ for induction of antitumor M1 macrophages. We measured the ability of type I and II IFNs to synergize with TLR agonists for transcription of inducible nitric oxide synthase (iNOS) mRNA and secretion of nitric oxide (NO) by mouse bone marrow-derived macrophages (BMDMs). An growth inhibition assay was used to measure both cytotoxic and cytostatic activity of activated macrophages against Lewis lung carcinoma (LLC) cancer cells. We found that both type I and II IFNs could synergize with TLR agonists in inducing macrophage-mediated inhibition of cancer cell growth, which was dependent on NO. The ability of high dose lipopolysaccharide (LPS) to induce tumoricidal activity in macrophages in the absence of IFN-γ was shown to depend on induction of autocrine type I IFNs. Antitumor M1 macrophages could also be generated in the absence of IFN-γ by a combination of two TLR ligands when using the TLR3 agonist poly(I:C) which induces autocrine type I IFNs. Finally, we show that encapsulation of poly(I:C) into nanoparticles improved its potency to induce M1 macrophages up to 100-fold. This study reveals the potential of type I IFNs for activation of antitumor macrophages and indicates new avenues for cancer immunotherapy based on type I IFN signaling, including combination of TLR agonists.

BackgroundThe efficacy of immune checkpoint inhibitors (ICIs), such as programmed cell death protein-1 (PD-1) or its ligand 1 (PD-L1) antibody, in hepatocellular carcinoma (HCC) is limited, and it is recommended that they be combined with other therapies. We evaluated the combination of pegylated interferon-α (Peg-IFNα) with PD-1 blockade in HCC mouse models.MethodsWe analyzed the effects of Peg-IFNα on tumor-infiltrating immune cells and PD-1 expression in the HCC immune microenvironment and examined the underlying mechanism of its unique effect on the PD-1 pathway. The in vivo efficacy of anti-PD-1 and Peg-IFNα was evaluated in both subcutaneous and orthotopic mouse models of HCC.ResultsThe combination of Peg-IFNα with PD-1 blockade dramatically enhanced T-cell infiltration, improved the efficacy of PD-1 antibody and prolonged mouse survival compared with PD-1 antibody monotherapy. Mechanistically, Peg-IFNα could recruit cytotoxic CD8+ T cells to infiltrate the HCC microenvironment by inducing tumor cells to secrete the chemokine CCL4. Nevertheless, the HCC microenvironment quickly overcame the immune responses by upregulating PD-1 expression in CD8+ T cells via the IFNα-IFNAR1-JAK1-STAT3 signaling pathway. The combination of PD-1 blockade with Peg-IFNα could restore the cytotoxic capacity of CD8+ T cells and exerted a significant synergistic effect on HCC.ConclusionThese results indicate that in addition to initiating the antitumor immune response itself, Peg-IFNα can also generate a microenvironment favoring PD-1 blockade. Thus, the combination of Peg-IFNα and PD-1 blockade can be a promising strategy for HCC.

Cerebral cytokinopathies are key examples of dysregulated cytokine responses. While mouse models with targeted production of individual cytokines have been pivotal in establishing a causal link between cytokines and disease- especially in the central nervous system-they often fail to replicate the complex inflammatory environments seen in various neuropathological conditions, such as neuromyelitis optica spectrum disorder, where multiple cytokines are upregulated. To address this, we developed a novel mouse model, GFAP-IL6-IFN mice, by combining transgenic mice with astrocyte-targeted production of IL-6 (GFAP-IL6 mice) and IFN-α (GFAP-IFN mice). Our findings reveal that chronic, low-level production of IFN-α, below the typical disease-inducing threshold, significantly accelerates disease progression in GFAP-IL6-IFN mice compared to GFAP-IL6 mice. The double transgenic mice exhibited progressive ataxia, persistent seizure-like episodes, and reduced survival. Remarkably, the clinical and pathological symptoms remained predominantly IL-6-driven and required the presence of adaptive immune cells. In summary, we demonstrate that subclinical levels of IFN-α can markedly exacerbate IL-6-mediated neurological disease, suggesting that future studies, should consider the combined effects of IL-6 and IFN-α.

Recent genome‐wide association studies suggest distinct roles for 12 human interferon‐alpha (IFN‐α) and 3 IFN‐λ subtypes that may be elucidated by defining the expression patterns of these sets of genes. To overcome the impediment of high homology among each of the sets, we designed a quantitative real‐time PCR assay that incorporates the use of molecular beacon and locked nucleic acid (LNA) probes, and in some instances, LNA oligonucleotide inhibitors. We then measured IFN subtype expression by human peripheral blood mononuclear cells and by purified monocytes, myeloid dendritic cells (mDC), plasmacytoid dendritic cells (pDC), and monocyte‐derived macrophages (MDM), and –dendritic cells (MDDC) in response to poly I:C, lipopolysaccharide (LPS), imiquimod and CpG oligonucleotides. We found that in response to poly I:C and LPS, monocytes, MDM and MDDC express a subtype pattern restricted primarily to IFN‐β and IFN‐λ1. In addition, while CpG elicited expression of all type I IFN subtypes by pDC, imiquimod did not. Furthermore, MDM and mDC highly express IFN‐λ, and the subtypes of IFN‐λ are expressed hierarchically in the order IFN‐λ1 followed by IFN‐λ2, and then IFN‐λ3. These data support a model of coordinated cell‐ and ligand‐specific expression of types I and III IFN. Defining IFN subtype expression profiles in a variety of contexts may elucidate specific roles for IFN subtypes as protective, therapeutic or pathogenic mediators.

Guanylate-binding protein 1 (GBP1) is a large GTPase of the dynamin superfamily involved in the regulation of membrane, cytoskeleton, and cell cycle progression dynamics. In many cell types, such as endothelial cells and monocytes, GBP1 expression is strongly provoked by interferon γ (IFNγ) and acts to restrain cellular proliferation in inflammatory contexts. In immunity, GBP1 activity is crucial for the maturation of autophagosomes infected by intracellular pathogens and the cellular response to pathogen-associated molecular patterns. In chronic inflammation, GBP1 activity inhibits endothelial cell proliferation even as it protects from IFNγ-induced apoptosis. A similar inhibition of proliferation has also been found in some tumor models, such as colorectal or prostate carcinoma mouse models. However, this activity appears to be context-dependent, as in other cancers, such as oral squamous cell carcinoma and ovarian cancer, GBP1 activity appears to anchor a complex, taxane chemotherapy resistance profile where its expression levels correlate with worsened prognosis in patients. This discrepancy in GBP1 function may be resolved by GBP1's involvement in the induction of a cellular senescence phenotype, wherein anti-proliferative signals coincide with potent resistance to apoptosis and set the stage for dysregulated proliferative mechanisms present in growing cancers to hijack GBP1 as a pro- chemotherapy treatment resistance (TXR) and pro-survival factor even in the face of continued cytotoxic treatment. While the structure of GBP1 has been extensively characterized, its roles in inflammation, TXR, senescence, and other biological functions remain under-investigated, although initial findings suggest that GBP1 is a compelling target for therapeutic intervention in a variety of conditions ranging from chronic inflammatory disorders to cancer.

Background and objective Though there are many advantages of pegylated interferon‐α (PegIFN‐α) treatment to chronic hepatitis B (CHB) patients, the response rate of PegIFN‐α is only 30 ~ 40%. Therefore, it is important to explore predictors at baseline and establish models to improve the response rate of PegIFN‐α. Methods We randomly divided 260 HBeAg‐positive CHB patients who were not previously treated and received PegIFN‐α monotherapy (180 μg/week) into a training dataset (70%) and testing dataset (30%). The intersect features were extracted from 50 routine laboratory variables using the recursive feature elimination method algorithm, Boruta algorithm, and Least Absolute Shrinkage and Selection Operator Regression algorithm in the training dataset. After that, based on the intersect features, eight machine learning models including Logistic Regression, k‐Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Gradient Boosting, Extreme Gradient Boosting (XGBoost), and Naïve Bayes were applied to evaluate HBeAg seroconversion in HBeAg‐positive CHB patients receiving PegIFN‐α monotherapy in the training dataset and testing dataset. Results XGBoost model showed the best performance, which had largest AUROC (0.900, 95% CI: 0.85–0.95 and 0.910, 95% CI: 0.84–0.98, in training dataset and testing dataset, respectively), and the best calibration curve performance to predict HBeAg seroconversion. The importance of XGBoost model indicated that treatment time contributed greatest to HBeAg seroconversion, followed by HBV DNA(log), HBeAg, HBeAb, HBcAb, ALT, triglyceride, and ALP. Conclusions XGBoost model based on common laboratory variables had good performance in predicting HBeAg seroconversion in HBeAg‐positive CHB patients receiving PegIFN‐α monotherapy. Flowchart of analysis process.