The hijacking of HBV by small extracellular vesicles inhibits M1 macrophages to facilitate immune evasion

Abstract

Small extracellular vesicles (sEVs) have the ability to transfer genetic material between cells, but their role in mediating HBV infection and regulating M1 macrophages to promote immune evasion remains unclear. In this study, we utilized PMA + LPS + IFN-γ to induce THP-1 into M1 macrophages. We then extracted sEVs from HepG2.2.15 cell and treated the M1 macrophages with these sEVs. QPCR detection revealed the presence of HBV-DNA in the M1 macrophages. Additionally, RT-qPCR and WB analysis demonstrated a significantly decreased in the expression of TLR4, NLRP3, pro-caspase-1, caspase-1p20, IL-1β and IL-18 in the M1 macrophages (P < 0.05). Furthermore, RT-qPCR results displayed high expression levels of that miR-146a and FEN-1 in the sEVs derived from HepG2.2.15 cells (P < 0.01). RT -qPCR and WB analysis showed that these sEVs enhanced the expression of FEN-1 or miR-146a in the M1 macrophages through miR-146a or FEN-1 (P < 0.05), while simultaneously reducing the expression of TLR4, NLRP3, caspase-1p20, IL-1β and IL-18 in the M1 macrophages (P < 0.05). In summary, our findings indicate that sEVs loaded with HBV inhibit the inflammatory function of M1 macrophages and promote immune escape. Additionally, miR-146a and FEN-1 present in the sEVs play a crucial role in this process.

Introduction

Today, there are approximately 257 million people worldwide who are infected with hepatitis B virus (HBV)¹, and there is an annual increase of 4.7 million new cases². The current antiviral therapy is unable to fully eradicate HBV from the body, which results the continuous progression of the disease and severe liver damage, including liver fibrosis, cirrhosis, and even hepatocellular carcinoma^3,4. The main reason for this is that the immune mechanism responsible for the persistent infection of HBV has not been fully understood. Existing evidence indicates that immune cell dysfunction plays a crucial role in both HBV persistent infection and liver inflammation⁵.

When viral infection occurs in the liver, hepatic macrophages initially recognize danger signals and recruit circulating monocytes to the liver. These monocytes then differentiate into macrophages, which have immunomodulatory, pathogen clearance and antiviral functions. These functions significantly impact the occurrence, development and regression of viral hepatitis⁶. Macrophage polarization is generally categorized into two phenotypes: classically activated M1 and alternatively activated M2. M1 macrophages secrete numerous inflammatory cytokines, that can inhibit HBV replication. On the other hand, M2 macrophages secrete a large number of anti-inflammatory cytokines, which suppress the host’s immune response and promote HBV replication⁷. Therefore, M1 and M2 macrophages play opposing roles in regulating in the host 's anti-HBV infection. M1 macrophages are primarily responsible for direct anti-HBV infection, and determines the course of disease development. Macrophages have two mechanisms for effectively recognizing pathogen-associated molecular patterns: surface TLR4, which triggers the synthesis of pro-IL-1β and pro-IL-18 through NF-κB, and the NLRP3 inflammasome in the cytoplasm, which plays a crucial role in antiviral infection. Activation of the NLRP3 inflammasome, by pathogens leads to caspase-1 activation, in the processing of inactive pro-IL-1β and pro-IL-18 into mature IL-1β and IL-18. These cytokines mediate the inflammatory response and are vital in the antiviral process^8–12. Current studies have confirmed that HBV can inhibit the inflammatory function of M1 macrophages¹³. As a crucial regulator of the interaction between the host and the virus, miR-146a is involved in HBV infection, replication and immune regulation. In the mouse model, miR-146a promotes viral persistence by inhibiting the inflammatory polarization of hepatic macrophages^14–16. Our previous study also discovered that miR-146a controls HBV replication through the crucial enzyme flap endonuclease1 (FEN-1)¹⁷. However, it is still unclear how HBV interferes with the anti-HBV effects of macrophages and enhances replication by targeting these key molecules. The specific molecular mechanism has not been fully elucidated. Consequently, further exploration of how HBV inhibits the function of M1 macrophage at a molecular level can offer more targets and strategies for antiviral therapy.

Small extracellular vesicles (sEVs)are extracellular vesicles derived from cells, ranging in diameter from 40 to 160 nm. They facilitate communication between cells by transferring biological macromolecules, functional proteins and nucleic acids. Additionally, they participate in a variety of biological functions, including antigen presentation, immune evasion, and tumor progression^18–22. Research has demonstrated that sEVs can alter their content of functional proteins or nucleic acids during viral infection occurs, subsequently impacting the host 's immune system. On the one hand, sEVs can contribute to an antiviral response by enhancing the activity of immune cells and facilitating the transfer of antiviral molecules between cells. On the other hand, sEVs can facilitate viral infection by directly or indirectly suppressing immune responses, inhibiting the signaling pathways mediated by cytokines, and reducing cytokine production²³. Thus, based on these findings, it is plausible to speculate that HBV exploits sEVs to inhibit the functionality of M1 macrophages, thereby promoting its replication.

In this study, we induced THP-1 cells differentiate into M1 macrophages. We then extracted sEVs from HepG2.2.15 cells, which are a cell line that stable replicates HBV. These sEVs were used to treat the M1 macrophages. We aimed to interfere with the expression of key molecules, specifically miR-146a and FEN-1, which regulate HBV replication in HepG2.2.15 cell-derived sEVs. The goal was to explore the molecular mechanism of HBV using sEVs to inhibit M1 macrophages and enhance replication.

Results

SEVs hijacked by HBV inhibit the expression of TLR4, NLRP3 and downstream factors in M1 macrophages

M1 macrophages treated with HepG2.2.15 cell-derived sEVs (HepG2.2.15-sEVs), M1 macrophages treated with HepG2 cell-derived sEVs (HepG2-sEVs), and M1 macrophages alone were used as controls. QPCR results showed that HBV-DNA was not detected in the M1 and M1 + HepG2-sEVs groups. In the M1 + HepG2.2.15-sEVs group, the expression of HBN-DNA in M1 macrophages was (2.92 ± 0.06) × 10⁴copies (Fig. 1A). RT-qPCR results revealed that the mRNA expression levels of TLR4 and NLRP3 in the M1 + HepG2.2.15-sEVs group were (0.38 ± 0.01) and (0.46 ± 0.06), respectively, which were significantly lower than those in the M1 group (1.01 ± 0.13) and (1.00 ± 0.06) (P < 0.01, Fig. 1B,C). They were also significantly lower than those in the M1 + HepG2-sEVs group (1.02 ± 0.24) and (1.15 ± 0.06) (P < 0.05, Fig. 1B,C). Western blot detection showed that the protein expression levels of TLR4, NLRP3 and caspase-1 p20 in the M1 + HepG2.2.15-sEVs group were (0.72 ± 0.06), (0.64 ± 0.04), and (0.48 ± 0.10), respectively, which were significantly lower than those in the M1 group (0.99 ± 0.07), (0.88 ± 0.05), and (0.91 ± 0.11) (P < 0.01, Fig. 1D), These levels were also significantly lower than those in the M1 + HepG2-sEVs group (0.96 ± 0.10), (0.86 ± 0.04), and (0.83 ± 0.04) (P < 0.05, Fig. 1D). The protein expression level of pro-caspase-1 in the M1 + HepG2.2.15-sEVs group was (1.05 ± 0.10), with no significant difference compared to the M1 group (1.01 ± 0.05) (P > 0.05, Fig. 1D), and no significant difference compared to the M1 + HepG2-sEVs group (1.00 ± 0.06) (P > 0.05, Fig. 1D). The mRNA expression levels of IL-1 β and IL-18 in the M1 + HepG2.2.15-sEVs group were (0.33 ± 0.03) and (0.41 ± 0.02), respectively, which were significantly lower than those in the M1 group (1.00 ± 0.09) and (1.00 ± 0.04) (P < 0.001, Fig. 1E,F). They were also significantly lower than those in the M1 + HepG2-sEVs group (1.09 ± 0.15) and (0.87 ± 0.05) (P < 0.05, Fig. 1E,F).

Fig. 1.

sEVs hijacked by HBV inhibit the expression of TLR4, NLRP3 and downstream factors in M1 macrophages. (A) QPCR was used to detect the expression of HBV-DNA. (B,C) RT-qPCR was used to detect the mRNA expression of TLR4 and NLRP3. (D)WB was used to detect the protein expression of TLR4, NLRP3, pro-caspase-1 and caspase-1 p20. (E,F) RT-qPCR was used to detect the mRNA expression of IL-1β and IL-18. *P < 0.05; **P < 0.01; ***P < 0.001; ^#P > 0.05.

SEVs hinder the expression of TLR4, NLRP3 and downstream factors in M1 macrophages via miR-146a

HepG2-sEVs and HepG2.2.15-sEVs were extracted using differential ultracentrifugation. RT-qPCR analysis showed that the expression level of miR-146a in HepG2.2.15-sEVs was (1.34 ± 0.11), which was significantly higher than that in HepG2-sEVs (0.18 ± 0.05) (P < 0.001, Fig. 2A). M1 macrophages were then treated with either a miR-146a mimic or miR-146a inhibitor in HepG2.2.15-sEVs, with a control group consisting of M1 macrophages treated with HepG2.2.15-sEVs alone. RT-qPCR results indicated that the expression levels of miR-146a and FEN-1 in the miR-146a inhibitor group were (0.16 ± 0.02) and (0.38 ± 0.11), respectively, which were significantly lower than those in the control group (1.04 ± 0.36) and (1.01 ± 0.16) (P < 0.05, Fig. 2B,C). Conversely, the expression levels of miR-146a and FEN-1 in the miR-146a mimic group were (4.59 ± 2.03) and (2.41 ± 0.50), respectively, which were significantly higher than those in the control group (1.04 ± 0.36) and (1.01 ± 0.16) (P < 0.05, Fig. 2B,C). Additionally, qPCR analysis showed that the HBV-DNA expression level in the miR-146a inhibitor group was (1.13 ± 0.08) × 10⁴ copies, which was significantly lower than that in the control group (1.59 ± 0.02) × 10⁴ copies (P < 0.01, Fig. 2D). The HBV-DNA expression level in the miR-146a mimic group was (1.63 ± 0.07) × 10⁴ copies, with no significant difference compared to the control group (1.59 ± 0.02) × 10⁴ copies(P > 0.05, Fig. 2D). RT-qPCR analysis revealed that the mRNA expression levels of TLR4 and NLRP3 in the miR-146a inhibitor group were (2.06 ± 0.22) and (2.24 ± 0.27), respectively, which were significantly higher than those in the control group (1.00 ± 0.05) and (1.00 ± 0.11) (P < 0.05, Fig. 2E,F). In contrast, the mRNA expression levels of TLR4 and NLRP3 in the miR-146a mimic group were (0.36 ± 0.02) and (0.35 ± 0.05), respectively, which were significantly lower than those in the control group (1.00 ± 0.05) and (1.00 ± 0.11) (P < 0.01, Fig. 2E,F). Western blot analysis showed that the protein expression levels of TLR4, NLRP3, and caspase-1 p20 in the miR-146a inhibitor group were (1.15 ± 0.06), (1.04 ± 0.11), and (1.15 ± 0.12), respectively, which were significantly higher than those in the control group (0.87 ± 0.10), (0.77 ± 0.04), and (0.63 ± 0.04) (P < 0.05, Fig. 2G). The protein expression levels of TLR4, NLRP3, and caspase-1 p20 in the miR-146a mimic group were (0.54 ± 0.01), (0.47 ± 0.09), and (0.35 ± 0.04), respectively, which were significantly lower than those in the control group (0.87 ± 0.10), (0.77 ± 0.04), and (0.63 ± 0.04) (P < 0.05, Fig. 2G). The protein expression level of pro-caspase-1 in the miR-146a inhibitor group was (0.84 ± 0.07), with no significant difference compared to the control group (0.88 ± 0.08) (P > 0.05, Fig. 2G). The protein expression level of pro-caspase-1 in the miR-146a mimic group was (0.78 ± 0.04), with no significant difference compared to the control group (0.88 ± 0.08) (P > 0.05, Fig. 2G). RT-qPCR analysis demonstrated that the mRNA expression levels of IL-1β and IL-18 in the miR-146a inhibitor group were (1.56 ± 0.10) and (1.69 ± 0.24), respectively, which were significantly higher than those in the control group (1.01 ± 0.13) and (1.00 ± 0.04) (P < 0.05, Fig. 2H,I). The mRNA expression levels of IL-1 β and IL-18 in the miR-146a mimic group were (0.35 ± 0.09) and (0.49 ± 0.04), respectively, which were significantly lower than those in the control group (1.01 ± 0.13) and (1.00 ± 0.04) (P < 0.01, Fig. 2H,I).

Fig. 2.

sEVs inhibit the expression of TLR4, NLRP3 and downstream factors in M1 macrophages through miR-146a. (A) RT-qPCR was used to detect the levels of miR-146a in HepG2-sEVs and HepG2.2.15-sEVs. (B,C) RT-qPCR was used to detect the levels of miR-146a and FEN-1. (D) qPCR was used to detect the levels of HBV-DNA. (E,F) RT-qPCR was used to detect the mRNA levels of TLR4 and NLRP3. (G) WB was used to detect the protein levels of TLR4, NLRP3, pro-caspase-1 and caspase-1 p20. (H,I) RT-qPCR was used to detect the mRNA levels of IL-1β and IL-18. *P < 0.05; **P < 0.01; ***P < 0.001; ^#P > 0.05.

SEVs inhibit the expression of TLR4, NLRP3 and downstream factors in M1 macrophages via FEN-1

HepG2-sEVs and HepG2.2.15-sEVs were extracted using differential ultracentrifugation. RT-qPCR analysis revealed that the expression level of FEN-1 in HepG2.2.15-sEVs was (1.79 ± 0.27), which was significantly higher than that in HepG2-sEVs (0.52 ± 0.05) (P < 0.01, Fig. 3A). FEN-1 siRNA and FEN-1 plasmid were separately transfected into HepG2.2.15-sEVs, and the treated M1 macrophages were analyzed. HepG2.2.15-sEVs treated M1 macrophages served as the control group. RT-qPCR analysis showed that the expression levels of FEN-1 and miR-146a in the FEN-1 siRNA group were (0.24 ± 0.06) and (0.32 ± 0.01), respectively, which were significantly lower than those in the control group (1.00 ± 0.06) and (1.00 ± 0.07) (P < 0.001, Fig. 3B,C). In contrast, the expression levels of FEN-1 and miR-146a in the FEN-1 plasmid group were (6.68 ± 0.83) and (1.86 ± 0.15), respectively, which were significantly higher than those in the control group (1.00 ± 0.06) and (1.00 ± 0.07) (P < 0.01, Fig. 3B,C). QPCR analysis showed that the HBV-DNA expression level in the FEN-1 siRNA group was (1.39 ± 0.09) × 10⁴ copies, which was significantly lower than that in the control group (1.75 ± 0.01) × 10⁴ copies (P < 0.05, Fig. 3D). The HBV-DNA expression level in the FEN-1 plasmid group was (1.80 ± 0.06) × 10⁴ copies, with no significant difference compared to the control group (1.75 ± 0.01) × 10⁴ copies (P > 0.05, Fig. 3D). RT-qPCR analysis revealed that the mRNA expression levels of TLR4 and NLRP3 in the FEN-1 siRNA group were (1.97 ± 0.06) and (1.55 ± 0.13), respectively, which were significantly higher than those in the control group (1.02 ± 0.22) and (1.01 ± 0.16) (P < 0.05, Fig. 3E,F). Conversely, the mRNA expression levels of TLR4 and NLRP3 in the FEN-1 plasmid group were (0.21 ± 0.03) and (0.23 ± 0.04), respectively, which were significantly lower than those in the control group (1.02 ± 0.22) and (1.01 ± 0.16) (P < 0.05, Fig. 3E,F). Western blot analysis showed that the protein expression levels of TLR4, NLRP3, and caspase-1 p20 in the FEN-1 siRNA group were (1.03 ± 0.08), (1.04 ± 0.04), and (1.17 ± 0.03), respectively, which were significantly higher than those in the control group (0.65 ± 0.05), (0.78 ± 0.06), and (0.69 ± 0.05) (P < 0.01, Fig. 3G). The protein expression levels of TLR4, NLRP3, and caspase-1 p20 in the FEN-1 plasmid group were (0.51 ± 0.04), (0.57 ± 0.06), and (0.48 ± 0.03), respectively, which were significantly lower than those in the control group (0.65 ± 0.05), (0.78 ± 0.06), and (0.69 ± 0.05) (P < 0.05, Fig. 3G). The protein expression level of pro-caspase-1 in the FEN-1 siRNA group was (0.98 ± 0.10), with no significant difference compared to the control group (1.10 ± 0.09) (P > 0.05, Fig. 3G). The protein expression level of pro-caspase-1 in the FEN-1 plasmid group was (1.01 ± 0.07), with no significant difference compared to the control group (1.10 ± 0.09) (P > 0.05, Fig. 3G). RT-qPCR analysis showed that the mRNA expression levels of IL-1 β and IL-18 in the FEN-1 siRNA group were (1.32 ± 0.06) and (3.10 ± 0.26), respectively, which were significantly higher than those in the control group (1.00 ± 0.10) and (1.01 ± 0.12) (P < 0.01, Fig. 3H,I). The mRNA expression levels of IL-1 β and IL-18 in the FEN-1 plasmid group were (0.30 ± 0.04) and (0.35 ± 0.03), respectively, which were significantly lower than those in the control group (1.00 ± 0.10) and (1.01 ± 0.12) (P < 0.01, Fig. 3H,I).

Fig. 3.

sEVs inhibit the expression of TLR4, NLRP3 and downstream factors in M1 macrophages via FEN-1. (A) RT-qPCR was used to detect the expression of FEN-1 in HepG2-sEVs and HepG2.2.15-sEVs. (B,C) RT-qPCR was used to detect the expression of FEN-1 and miR-146a. (D) qPCR was used to detect the expression of HBV-DNA. (E,F) RT-qPCR was used to detect the mRNA expression of TLR4 and NLRP3. (G) WB was used to detect the protein expression of TLR4, NLRP3, pro-caspase-1 and caspase-1 p20. (H,I) RT-qPCR was used to detect the mRNA expression of IL-1β and IL-18. *P < 0.05; **P < 0.01; ***P < 0.001; ^#P > 0.05.

Discussion

Small extracellular vesicles (sEVs) are an important means of intercellular signal transduction, regulating antigen presentation, inducing immune activation and immunosuppression, and playing a crucial role in various physiological and pathological processes^24,25. Recent studies have discovered that sEVs produced by HBV-infected hepatocytes can cause immune tolerance of NK cells²⁶. Additionally, sEVs secreted by HBV-infected hepatocytes can impact the immune function of monocytes by increasing the expression of programmed death-ligand 1 (PD-L1) in monocytes, while concurrently decreasing CD69 expression²⁷. These studies have revealed the significant role of sEVs in influencing immune cell function and promoting HBV replication. As a result, our focus is on sEVs as a mediator functional molecule. Moreover, macrophages are widely distributed in the body as important innate immune cells that play a crucial role in maintaining homeostasis and defending against pathogen invasion. In a stable condition, liver resident macrophages make up 80% of systemic macrophages^28,29. Based on this, it is particularly important to investigate the mechanism by which sEVs regulate macrophage function and subsequently influence HBV replication.

In this study, we discovered that sEVs produced by HBV-infected liver cells suppressed the expression of TLR4, NLRP3, caspase-1 p20, IL-1β and IL-18 in M1 macrophages. TLR4 found on the surface of macrophages, is a pattern recognition receptor (PRR) and the main receptor of endotoxin. It effectively recognizes pathogen-associated molecular patterns (PAMPs) and is closely associated with bacterial and viral infection⁸. TLR4 activates signaling pathways, both MyD88-dependent and MyD88-independent, which promoted the production of inflammatory factors and type I interferon (IFN)³⁰. By inhibiting the expression of TLR4 in M1 macrophages, the immune inflammatory response to HBV infection is reduced, which hinders the clearance of HBV. The NLRP3 inflammasome is another pivotal player in the inflammatory response³¹. Various pathogens and cell damage can trigger the activation of the NLRP3 inflammasome, leading to the activation of caspase-1. This activation processes pro-IL-1β and pro-IL-18 into mature forms, IL-1β and IL-18 respectively¹². These cytokines play a significant role in mediating the inflammatory response and are crucial for the control and elimination of HBV³². Hence, the decreased expression of NLRP3, caspase-1 p20, IL-1β and IL-18 in M1 macrophages hampers the inflammatory function of M1 macrophages and facilitates the immune escape evasion of HBV.

Our previous studies have confirmed that miR-146a and FEN-1 are highly expressed in hepatocytes infected with HBV. Additionally, these two molecules form a positive feedback mechanism that enhances HBV replication¹⁷. The results of this study also showed that miR-146a and FEN-1 are highly expressed in sEVs derived from HBV-infected hepatocytes. Therefore, we speculate that the high expression of miR-146a and FEN-1 in these sEVs may create a microenvironment that is more conducive to HBV replication. This suggests that miR-146a and FEN-1 in HBV-infected hepatocyte-derived sEVs may play an important role in immune regulation.

To investigate this further, we interfered with miR-146a or FEN-1 in HBV-infected hepatocyte-derived sEVs and incubated them with M1 macrophages. It was found that HBV-infected hepatocyte-derived sEVs promoted the expression of FEN-1 or miR-146a in M1 macrophages through miR-146a or FEN-1, and also reduced the expression of TLR4, NLRP3, caspase-1 p20, IL-1β and IL-18 in M1 macrophages. miR-146a is known to negatively regulates toll-like receptors (TLRs) in immune cells and plays a role in the immune system’s dynamic balance³³。Previous studies have also shown that miR-146a, as a key regulator of host-virus interactions, is involved in HBV infection, replication, and immune regulation. In mouse model, miR-146a promotes viral persistence by inhibiting the inflammatory polarization of hepatic macrophages^14–16. Combined with the results in this study, it is reasonable to believe that HBV-infected hepatocyte-derived sEVs promote HBV immune escape by interfering with M1 macrophage function through miR-146a. FEN-1 is an enzyme involved in the repair of HBV relaxed circular DNA (rcDNA) into covalently closed circular DNA (cccDNA), which promotes HBV replication³⁴. HBV-infected hepatocyte-derived sEVs also highly express FEN-1. Combining the results of intervention treatment, the most reasonable explanation is that FEN-1 promotes the increase of miR-146a in M1 macrophages, which in turn interferes with the function of M1 macrophages and promotes HBV immune escape. Interestingly, overexpression of miR-146a or FEN-1 in HBV-infected hepatocytes derived sEVs, and then treated M1 macrophages. Although the function of M1 macrophages was disturbed, the expression of HBV-DNA in M1 macrophages did not increase. This may be because M1 macrophages, as innate immune cells, are host-targeted by HBV for infection and are therefore not suitable for HBV replication. The presence of transient HBV seems to be more closely related to immune regulation. However, further molecular biology research is needed to determine the exact mechanism.

In summary, we have demonstrated the molecular mechanism by which sEVs derived from HBV-infected hepatocyte inhibit the function of M1 macrophage and allow HBV to evade the immune system attack (as depicted in Fig. 4). sEVs derived from HBV-infected hepatocytes carry high levels of miR-146a and FEN-1, which enter M1 macrophages via sEVs. Inside M1 macrophages, miR-146a and FEN-1 work together a mutually reinforcing positive feedback mechanism, leading to a decrease in reducing the expression of TLR4, NLRP3, caspase-1 p20, IL-1β and IL-18. This decrease inhibits the function of M1 macrophages facilitating HBV immune evasion and promoting its replication.

Fig. 4.

A mechanistic map illustrating how sEVs inhibiting the inflammatory function of M1 macrophages. Hepatocytes infected with HBV form sEVs through multivesicular bodies (MVB). These sEVs fuse with the plasma membrane, releasing sEVs that contain HBV components. These sEVs then transmit HBV to M1 macrophages. In M1 macrophages, sEVs decrease the expression of TLR4 and NLRP3 through miR-146a and FEN-1. This further inhibits the activation of caspase-1, reduces the expression of IL-1β and IL-18, and promotes the immune escape of HBV.

Methods

Preparation of M1 macrophages

THP-1 cells (Shanghai Zhong Qiao Xin Zhou Biotechnology Co Ltd., China) were cultured in RPMI 1640 medium (GIBCO, USA), which contained 10% fetal bovine serum (BI, USA), 50umol/L β-mercaptoethanol, 100U/mL penicillin and 100 g/mL streptomycin, at a temperature of 37° C and with 5% CO2. The THP-1 cells were continuously stimulated with 100 ng/mL PMA (Beyotime Biotechnology, China) for 48 h. Afterward, they were washed twice with sterile PBS and then cultured in 1640 complete medium without PMA for 24 h in order to induce differentiation into M0 macrophages. The M0 macrophages were subsequently continuously stimulated with 100 ng/mL LPS (Beijing Solarbio Science & Technology Co Ltd., China) + 20 ng/mL INF-γ (Novoprotein Scientific Inc., China) for 24 h to induce M1 macrophages.

Extraction of sEVs using differential ultracentrifugation

HepG2 (cat. no. HB-ATCC8065) and HepG2.2.15 (cat. no. YB-ATCC2242) hepatoma cells were cultured in an incubator with DMEM (GIBCO, USA) containing 10% exosome-free FBS (SBI, USA), 100 U/mL penicillin, and 100 g/mL streptomycin at 37 °C with 5% CO2. G418 (Shanghai Yuanye Biological Co Ltd., China) was added to the medium to maintain stable HBV replication in HepG2.2.15 cells. After cells reached 80 to 90% confluence, supernatants were collected after centrifugation (2000 g for 30 min, and then 12,000 g for 45 min), and were passed through a 0.22 μm filter. The supernatant was then subjected to ultracentrifugation (100,000 g for 120 min at 4 °C; Beckman L-100XP, Beckman Coulter). The resulting pellets, which were enriched in sEVs, were resuspended in 1 × PBS, and then subjected to a second round of ultracentrifugation (110,000 g for 120 min at 4 °C). Then, the supernatant was discarded, and the sEVs -enriched pellet was washed in PBS and measure the concentration of small extracellular vesicles using BCA method, then adjust the concentration to 200 ng/μL with PBS and place it at – 80 °C for future use.

Transmission electron microscopy of sEVs

A 10 μL suspension of exosomes was added dropwise onto a carbon-coated copper net and was allowed to adsorb for 5 min. Then, 10 μL of phosphotungstic acid staining solution (20 g/L, Beijing Solarbio Science & Technology Co Ltd., China) was added dropwise onto the copper net for 50 to 70 s. Excess PBS was removed with a filter paper, the sample was dried overnight, and was then observed using a transmission electron microscope (Hitachi HT7700, Japan).

Nanoparticle tracking analysis

The sEVs obtained through differential ultracentrifugation were diluted 10 times with PBS. The diluted sEVs were then injected into the Nanoparticle Tracking Analyzer (Particle Metrix-Zeta, Germany) using a syringe to assess their particle size.

Flow cytometer analysis

The adherent cells were digested using trypsin, washed twice with PBS, centrifuged at 500 g for 5 min, and the cell concentration was adjusted to 2 × 10⁵/ mL with PBS. The cells were then mixed and 300uL of cell suspension was added to each flow tube. The tubes were incubated with the primary antibody at room temperature for 30 min (CD68 Rabbit mAb, HLA-DR Rabbit mAb, CD86 Rabbit mAb, CD163 Rabbit mAb, CD206 Rabbit mAb; ZEN-Bioscience Company, Chengdu, China), followed by two washes with PBS. Next, the tubes were incubated with FITC-labeled goat anti-rabbit IgG secondary antibody (ZEN-Bioscience Company, Chengdu, China) at room temperature for 45 min. After another two washes with PBS, the cells were resuspended in 300uL of PBS, filtered with a 100um nylon filter membrane, 100um nylon filter membrane filtration, using flow cytometry (Agilent, USA) detection. During testing on the machine, NovoExpress software was used for analysis. The whole cell population was selected as P1 gate using flow cytometry forward light FSC and lateral light SSC display to remove cell debris. FITC-H was used as the abscissa and count was used as the ordinate. The expression differences of CD68, CD86, CD163, CD206 and HLA-DR were compared based on the mean fluorescence intensity (MFI).

Western Blot (WB) analysis

The total protein was extracted using RIPA lysis buffer, and the protein concentration was quantified using BCA method. A total of 30 ug of protein was loaded to an SDS-PAGE gel for electrophoresis and transferred to PVDF membrane. The membrane was then blocked with 5% milk powder for 1 h. Following this, it was incubated with the primary antibody (CD9 Rabbit mAb, CD63 Rabbit mAb, Alix Rabbit pAb, TLR4 Rabbit mAb, NLRP3 Rabbit mAb, β-actin Rabbit mAb, ABclonal Technology, Wuhan, China; caspase-1 Rabbit pAb, HUABIO, Hangzhou, china) overnight, washed three times with TBST for 10 min each time, and subsequently incubated with the secondary antibody (HRP-labeled sheep anti-rabbit, ZEN-Bioscience Company, Chengdu, China) for 1 h. The membrane was washed three times with TBST for 10 min each time, before being exposed for imaging.

HBV DNA detection by qPCR

The qPCR was used to measure HBV DNA. The reaction system consisted of ddH2O (8.5 μL), SYBR Green Master (12.5 μL), HBV DNA primer (Forward 1 μL, Reverse 1 μL), and DNA extraction solution (2 μL) in a total volume of 25 μL. A BIO-RAD Real-time PCR (CFXconnect, USA) system was used. The amplification conditions were: 95 °C for 10 min followed by 35 to 40 cycles at 95 °C for 10 s, 64 °C for 30 s, and 72 °C for 32 s. The absolute copy number was calculated using a standard curve, and each count was performed 3 times. The primers sequences are shown in Table 1.

Table 1.

Primer sequences.

Target genes	Forward primers (5′→3′)	Reverse primers (5′→3′)
IL-1β	ATGATGGCTTATTACAGTGGCAA	GTCGGAGATTCGTAGCTGGA
IL-6	ACTCACCTCTTCAGAACGAATTG	CCATCTTTGGAAGGTTCAGGTTG
TNF-α	GAGGCCAAGCCCTGGTATG	CGGGCCGATTGATCTCAGC
IL-10	GAC TTT AAG GGT TAC CTG GGT TG	TCA CAT GCG CCT TGA TGT CTG
TGF-β	CTA ATG GTG GAA ACC CAC AAC G	TAT CGC CAG GAA TTG TTG CTG
TLR4	AGACCTGTCCCTGAACCCTAT	CGATGGACTTCTAAACCAGCCA
NLRP3	GATCTTCGCTGCGATCAACAG	CGTGCATTATCTGAACCCCAC
IL-18	TCTTCATTGACCAAGGAAATCGG	TCCGGGGTGCATTATCTCTAC
β-actin	CTCCATCCTGGCCTCGCTGT	GCTGTCACCTTCACCGTTCC
HBV DNA	ACCGACCTTGAGGCATACTT	GCCTACAGCCTCCTAGTACA
FEN-1	CAAAGGCCAGTCATCCCTCCT	GCTGTCACCTTCACCGTTCC
miR-146a	GAGAACTGAATTCCATGGGT	GCCTACAGCCTCCTAGTACA
U6	GCTTCGGCAGCACATATACT	AACGCTTCACGAATTTGCGT

mRNA detection by qRT-PCR

The TRIzol™ Reagent (700µL, Thermo Fisher Scientific, USA) was used to extract total RNA. Then, 1 μg RNA was extracted using the Revert Aid™ First Strand cDNA Synthesis Kit (Thermo Fisher Scientific, USA) to reverse transcribe cDNA. The reaction system consisted of random primer (1 μL), template RNA (1 μg), 5 × Reaction buffer (4 μL), 10 mM dNTP mix (2 μL), RNase inhibitor (1 μL) and RevertAid RT (1 μL). The amplification conditions were: 65 °C for 5 min, 25 °C for 5 min, 42 °C for 60 min, and 70 °C for 5 min. BIO-RAD Real-time PCR (CFXconnect, USA) was used and the reaction system consisted of ddH2O (8.5 μL), SYBR Green Master (12.5 μL, CWBIO, Beijing, China), primers(Forward 1 μL, Reverse 1 μL), and a DNA extraction solution (2 μL) in a total volume of 25 μL. The amplification conditions were: 95 °C for 10 min, and then 40 cycles of 95 °C for 15 s and 60 °C for 1 min. The relative expression was calculated using the 2^−ΔΔCt method. The primers sequences are shown in Table 1.

MicroRNA detection by qRT-PCR

The QIAzol™ Reagent (700µL, Thermo Fisher Scientific, USA) was used to extract microRNA. Then, the Applied Biosystems TaqMan™ MicroRNA Reverse Transcription Kit (Thermo Fisher Scientific, USA) was used to reverse transcribe cDNA. The reaction system consisted of 100 mM dNTP (0.15 μL), MultiScribe Reverse Transcriptase (1 μL), 10 × buffer (1.5 μL), nuclease-inhibitor (0.19 μL), 5 × Primer (U6/miR-146a, 3 μL), RNA sample (9.16 μL) in a total volume 15 μL. The amplification conditions were: 16 °C for 30 min, 42 °C for 30 min, and 85 °C for 5 min. The BIO-RAD Real-time PCR (CFXconnect, USA) system was used with the TaqMan Probe (Chengdu Utysen Bioengineering Co. Ltd. China). The reaction system consisted of TaqMan PCR Master (12.5 μL), nuclease-free water (5.7 μL), 16 × Primer and probe mix (1.8 μL), and a cDNA sample (5 μL) in a total volume of 25 μL. The amplification conditions were: 95 °C for 10 min, and then 40 cycles of 95 °C for 1 s, 52 °C for 30 s, and 60 °C for 30 s. The relative expression was calculated using the 2 ^-ΔΔCt method. The primers sequences are shown in Table 1.

Statistical analysis

All data were presented as mean ± standard error (SE) from three independent experiments. SPSS Statistics 26 was utilized for conducting statistical analysis on the data, while GraphPad Prism8.0 software was employed for data visualization. Student's t-test was employed to compare between the two groups. A significance level of P < 0.05 was employed to determine statistical significance.

Author contributions

All authors contributed to the study conception and design. W.P. consulted the literature, and conceived and designed the present study. Z.Z., J.L., L.Y., R.Z., W.P. performed the experiments. Z.Z. and W.P. confirm the authenticity of all the raw data. All authors read and approved the final manuscript.

Funding

This research was funded by 2019 National Pre-Research Project of NSMC, Grant No. CBY19-Z07, and Science and Technology Strategic Cooperation Project between Nanchong City and NSMC, Grant No. 22SXQT0318.

Data availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-70924-3.