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Back to Journal »Infection and Resistance» Volume 14

DIA-based quantitative proteomics comparative analysis of serum proteins between hepatitis B virus genotypes B and C infections

Authors: Chen Yi, Wei Ding, Deng Min

Published on November 9, 2021, Volume 2021: 14 pages, 4701-4715 pages

DOI https://doi.org/10.2147/IDR.S335666

Single anonymous peer review

Editor approved for publication: Professor Suresh Antony

Yunqing Chen,1,2 Dahai Wei,1–3 Min Deng1–3 1 Department of Infectious Diseases, Affiliated Hospital of Jiaxing University, Jiaxing, People's Republic of China; 2 Department of Infectious Diseases, First Hospital of Jiaxing City, Jiaxing City, People's Republic of China; 3 Liver Diseases, Affiliated Hospital of Jiaxing University Institute, Jiaxing, People’s Republic of China Author: Wei Dahai; Deng Min 314001, Affiliated Hospital of Jiaxing University, 1882 Zhonghuan South Road, Jiaxing City, People’s Republic of China Tel/Fax 86-573-89975669 Email [email protected]; [email protected] Purpose: In In clinical practice, the clinicopathological characteristics and results of patients infected with hepatitis B virus (HBV) differ between genotypes B and C. However, little is known about the underlying mechanisms and differences in specific biological pathways associated with hepatitis B virus (HBV) infection. Different genotypes. This study aims to compare the serum protein profiles of HBV genotype B infected persons and HBV genotype C infected persons. Patients and methods: A total of 54 serum samples and healthy controls from chronic HBV genotype B infection and chronic HBV genotype C infection were used for proteomics analysis (n = 18 samples per group). Analyze serum proteomics characteristics using data independent acquisition (DIA)-based liquid chromatography-mass spectrometry to identify serum samples from HBV patients infected with HBV genotype B and the differentially expressed proteins (up-regulated or Down at least 1.5 times). Results: We identified 1,010 proteins, of which 53 proteins were differentially expressed between healthy controls and serum samples from patients with HBV genotype B infection, and 59 proteins were between healthy control samples and serum samples from patients with hepatitis B virus infection. Differential expression. Patients with HBV genotype C infection. In addition, our results indicate that the two proteins identified as differentially expressed (VWF and C8B) have potential as biomarkers for distinguishing between genotype B-infected HBV patients and genotype C-infected patients. Conclusion: The results of our quantitative proteomics analysis based on DIA show that HBV genotypes B and C are related to different molecular profiles, which can provide basic information for further detailed study of the molecular mechanisms behind these differences. Keywords: serum protein profile, complement and coagulation cascade, von Willebrand factor, complement C8 β chain

Hepatitis B virus (HBV) is an enveloped, non-cytopathic, hepatotropic, partially double-stranded DNA virus, belonging to the Orthohepadnavirus family (family: Hepatotropic DNA virus family). 1,2 Despite significant progress in the availability of safe vaccines and antiviral therapies against HBV, the virus still affects approximately 257 million people worldwide and causes approximately 887,000 deaths each year. 3 HBV infection is related to acute and chronic liver failure, which can lead to chronic hepatitis or fulminant hepatitis, and put patients at a high risk of developing advanced liver fibrosis and cirrhosis, and even hepatocellular carcinoma (HCC). 1,2,4 Many virus-derived factors that may affect the clinical manifestations or disease prognosis during chronic HBV infection have been identified; among them, the virus genotype and HBV mutations that attribute the virus to a certain phenotype have been reported as viral onset The key determinants of the mechanism include changes in host immune recognition, increased virulence as HBV replication increases, and promotion of cell attachment or penetration. 5–7

So far, 10 HBV genotypes (AJ) have been characterized, and different geographical and ethnic distributions have been assigned worldwide based on more than 8% of the genotype sequence differences. Genotypes B and C dominate in Asia and the Pacific, including China. 8,9 Different genotypes have different effects on the severity of the disease, the course of the disease, the possibility of complications, response to treatment, and possible vaccination. 10-12 It has been shown that compared with patients infected with genotype B, patients infected with genotype C have higher basic core promoter A1762T/G1764A mutations and spontaneous tyrosine-methionine-aspartic acid-aspartine The prevalence of acid (YMDD) mutations. 13,14 A study of 332 patients in Hong Kong, Kong, reported that patients with genotype C had a significantly higher prevalence of hepatitis B e antigen (HBeAg) (53% vs. 69%, P <0.01). And the cumulative rate of HBeAg seroconversion during the follow-up period was lower than that of genotype B infection. Therefore, patients infected with genotype C have delayed HBeAg seroconversion, so the duration of high viral load is significantly longer than that of patients with genotype B. 15 In another study among 150 patients with chronic HBV infection in China, the authors found that patients with genotype C had a higher level of virus replication (6.87 ± 0.35 vs 5.03 ± 0.55 log10 copies/mL, P <0.01). Acid transferase (ALT) (500.35 ± 81.81 vs 269.51 ± 46.62 U/L, P <0.01), non-specific CTL (19.72 ± 1.07% vs 16.65 ± 2.21%, P <0.01), but HBV-specific cytotoxic T lymph Low levels of cell (CTL) (CTL) (0.033%) and 0.37 ± 0.03%, P <0.01), follicular helper T cells (Tfh) (3.85 ± 2.43% and 5.91 ± 1.84%, percentage of CD4 T lymphocytes) , P <0.01) and interleukin-21 (IL-21) (15.80 ± 2.44 vs 43.26 ± 19.70 ng/L, P <0.01). 16 In addition, some specific viral mutations, high HBV viral load, and quantitative hepatitis B surface antigen (HBsAg) levels may be independently associated with more live disease-related complications and higher chances of HCC conversion. The frequency of HBV genotype C is significantly higher than that of genotype B patients. 17,18 Taken together, these indicate that there are significant differences in clinical manifestations in patients with si infection of these two genotypes.

HBV modifies the host's transcription and translation mechanisms and forces the host to meet the requirements of the virus during the infection process, thereby regulating the synthesis of macromolecules in the host. 4,19,20 These requirements may lead to epigenetic modifications associated with almost all biological processes. Every step of the HBV life cycle, from entry to secretion. 10,19,21 A large number of epidemiological studies have shown that patients infected with HBV genotype C have significantly lower T helper 1 (Th1) cytokine levels, while T cytokine levels are higher. Th2 cytokines produce more T cells than patients with genotype B, indicating that HBV genotype C induces larger Th2 and smaller Th1 responses than genotype B. 22-24 In our previous quantitative proteomics research, we found that HCC induced by protein B, which is dysregulated in the HBV genotype, is mainly involved in biological processes, such as response to toxins, RNA splicing, and cellular macromolecular complex assembly, wh The dysregulated proteins in HCC induced by genotype C are mainly related to organic acid catabolism, carboxylic acid catabolism, and alcohol biosynthesis. 25 These results indicate that there are significant differences in molecular pathogenesis between genotype B and genotype HBV patients. Infection with genotype C. However, the precise molecular mechanisms associated with these differences remain largely unknown.

Comparative proteomics methods based on data independent acquisition (DIA) liquid chromatography-tandem mass spectrometry (LC-MS/MS) are commonly used to analyze the response of human, animal, and plant hosts during viral infection. 26,27 In addition, DIA-quantitative proteomics based on quantitative proteomics can be used to screen and identify key protein biomarkers for early disease recognition, diagnosis, monitoring and treatment. 28-30 Therefore, serum proteomics analysis provides a comprehensive understanding of the host factors involved in viral infections, and provides insights into signal transduction pathway changes, and improves our understanding of the molecular pathogenesis of HBV infection. However, there has been no report so far that quantitative proteomics analysis has been applied to study the difference in serum protein profiles between HBV genotype B and genotype C infections. Here, in order to explain the molecular differences in host resistance to genotype B and C HBV, we performed DIA-based quantitative proteomics on the serum protein expression profile between HBV genotype B infected patients and HBV genotype C infected patients comparative analysis. Results The results of this study will help to understand the molecular differences between patients infected with the two HBV genotypes, and may provide basic information for further detailed study of the molecular mechanisms behind these differences.

The study population included healthy controls and eligible patients with chronic hepatitis B (CHB) who were determined to be seropositive for HBV infection at the Department of Infectious Diseases, Jiaxing University Hospital, East China from January 2018 to June 2020. The HBV genotyping of all samples was performed by real-time fluorescent PCR using a commercially available HBV genotyping kit (Shanghai Fosun Pharmaceutical Co., Ltd., Shanghai, China) according to the manufacturer's recommendation and previous description. 9,25 The study excluded patients who had received nucleoside analogues or interferon therapy within the previous 2 years. At the same time, patients with HIV, HAV, HCV, HDV or other viral infections are also excluded, and those with liver disease (such as liver cirrhosis or HCC) are also excluded. The patient’s anonymous clinical information, including biochemical data, is shown in Table 1. This study was reviewed and approved by the Institutional Review Committee of the Affiliated Hospital of Jiaxing University, and all participants provided written informed consent (approval number: LS2019-327) before enrollment. Table 1 Baseline characteristics of patients participating in this study

Table 1 Baseline characteristics of patients participating in this study

Serum samples of 18 healthy controls and 36 patients infected with HBV genotype B or C (n = 18 for each genotype) were divided into three groups: healthy controls (group A, n = 18), infected with HBV gene Type B (HBV-B) (group B, n = 18) and patients infected with HBV genotype C (HBV-C) (group C, n = 18), according to standard operating procedures to minimize pre-analysis mutations. For each group, every 6 separate samples containing equal volumes of serum were mixed, and then the combined serum was divided into high abundance on the human multiple affinity removal system column (Agilent Technologies, Santa Clara, California, USA) And low-abundance protein parts) according to the manufacturer's instructions. Each group obtained six repetitive protein extracts to minimize the impact of individual differences in patients (Figure S1).

The high-abundance and low-abundance proteins were collected in a 5 kDa ultrafiltration tube (Sartorius, Germany) for the desalination and concentration of high-abundance and low-abundance components. The protein was precipitated with SDT lysis buffer at 95°C for 15 minutes, and then centrifuged at 14,000 g for 20 minutes. Use the BCA protein determination kit (Bio-Rad, USA) to determine the protein concentration, and store the resulting supernatant at -80 °C until use.

Protein digestion was performed using the previously described filter-assisted sample preparation method. 30 In short, 100 μL of iodoacetamide (IAA; 100 mM in UA buffer) was added to the protein sample, and then incubated for 30 minutes in the dark at room temperature. The protein was then digested with 4 μg trypsin (Promega, USA) in 40 μL 25 mM NH4HCO3 buffer at 37°C overnight, desalted using a C18 cartridge (Sigma, USA), and then dried under vacuum.

A Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific, USA) equipped with an Easy-nLC 1200 chromatography system (Thermo Fisher Scientific) was used to analyze all the fractions generated from the data-related acquisition (DDA) library. The peptide (1.5 μg) was first loaded on the EASY-Spray C18 Trap Column (Thermo Fisher Scientific), and then on the EASY-Spray C18 LC analytical column (Thermo Fisher Scientific) using buffer B (84% acetonitrile and 0.1% formic acid), The flow rate is 250 nL/min for 120 minutes. In short, the full MS scan range is 300 to 1800 m/z, with a resolution of 60,000 at m/z 200. The automatic gain control (AGC) is set to 3e6, and the maximum ion implantation time (MIT) is set to 25 milliseconds. After each complete MS-SIM scan, 20 ddMS2 scans are performed. The resolution of the MS2 scan is 15,000, the AGC target is 5e4, the MIT is 250 ms, and the normalized collision energy is 30 eV.

The peptides from each sample were analyzed by LC-MS/MS in DIA mode. Each DIA cycle includes a complete MS-SIM scan and 30 DIA scans, covering a mass range of 350-1800 m/z, and a resolution of 120,000 at 200 m/z. In profile mode, MIT is set to 50 ms and AGC is set to 3e6. The resolution of the DIA scan is 15,000 (AGC target: 3e6; MIT: automatic; normalized collision energy: 30 eV). The run time is 120 minutes, linear gradient buffer B, and the flow rate is 250 nL/min. To monitor MS performance, QC samples were injected in DIA mode at the beginning of the analysis and after every six injections throughout the experiment.

As mentioned earlier, the Spectronaut software (version 14.4.200727.47784; Biognosys, Switzerland) was used to search the DDA library data against the FASTA sequence database. 27 The human proteome database in UniProt (2020/11/22, 9951 sequence) has used iRT peptide sequences attached. Search using the following parameters: enzymes, trypsin; biggest missed cleavage, 2; fixed modification, carbamoyl (C); dynamic modification, oxidation (M) and acetyl (protein N-terminus). In addition, all data are reported based on the 99% confidence level of protein identification (false discovery rate [FDR] <0.01) (Figure S2). Then import the analysis results into Spectronaut Pulsar X (version 12.0.20491.4; Biognosys) to generate a spectral library.

Use Spectronaut software (version 14.4.200727.47784) to further search the original DIA data against the above-mentioned spectral library. For the main software parameters, retention time prediction is set to dynamic iRT, and interference MS2 level correction and cross-run normalization are enabled at the same time. The FDR threshold for the peptide level was set to 1% to obtain significant quantitative data. All MS raw data has been stored in the ProteomeXchange alliance through the PRIDE partner repository with the data set identifier PXD025968.

Significantly differentially expressed proteins are defined as proteins that show a fold change of ≥1.5 or ≤0.67 and a paired t-test P value <0.05. Use Cluster 3.0 (http://bonsai.hgc.jp/~mdehoon/software/cluster/software.htm) and Java Tree View software (http://jtreeview.sourceforge.net) to perform hierarchical aggregation of all differentially expressed proteins. Class analysis). Based on the functional annotations of biological processes, molecular functions and cellular components, the software program Blast2GO (http://www.blast2 go.com/b2 g home) was used for gene ontology (GO) and InterPro (IPR) analysis.

The Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://geneontology.org/) is used to annotate protein families and pathway tools/cards through the KEGG automatic annotation server (KAAS, https://www.genom e.jp/) S/).

All statistical analyses were performed using the Social Science Statistical Package (SPSS) 22.0 software (SPSS Inc., Chicago, IL, USA). Quantitative data is expressed as mean ± standard deviation (SD). The statistical significance of the difference was analyzed using Student's t-test for comparison between two groups, and one-way analysis of variance (ANOVA) was used for comparison between multiple groups. If the two-tailed p-value is less than 0.05, the difference is considered statistically significant.

In order to verify the results of the proteomics study, enzyme-linked immunosorbent assay (ELISA) and automated biochemical analysis as described above were performed on the proteins that showed significantly different levels in the two groups. 27 All serum samples collected from the three groups were used for the selected dysregulated protein using a commercially available ELISA kit (Abcam, USA) according to the manufacturer's protocol. The ELISA for each sample was performed in triplicate. Use a microplate reader (BioTek ELx800) to read the optical density value at 450 nm, and automatically calculate the concentration based on the standard curve and dilution factor. The coefficient of variation between and within the measurement is less than 5%.

A total of 54 serum samples from patients with chronic HBV genotype B and C infection and healthy controls were used for proteomics analysis, with 18 samples in each group. In each group, according to the genotype of HBV, the patients were further randomly divided into three subgroups with 6 persons in each group. No significant differences were found between the three groups in age, sex ratio or alkaline phosphatase (ALP), total bile acid (TBA) and fibrinogen (FIB) levels. However, compared with healthy controls or patients in the HBV-B group, the ALT, aspartate transferase (AST), γ-glutamyltransferase (GGT) and HBeAg levels of the HBV-C group patients, platelet laboratory The results are significantly different. (PLT) count, and AST-PLT Ratio Index (APRI) (Table 1).

Through DIA-based quantitative proteomics analysis, we identified 1923 unique and abundant proteins from the 9,951 peptides in UniProt's human proteome database (Figure 1A and B). Using the results obtained by Spectronaut software and the integrated Andromeda search engine, we quantified 1303, 1206, and 1374 proteins in three replicates used for DIA-based quantitative proteomics analysis. A total of 1010 proteins in the three groups overlap, accounting for 63.72% of the total quantitative protein (Figure 1C). Figure 1 Identification of serum proteins in HBV patients infected with genotype B and genotype C of HBV. (A) The number of peptides identified in 3 repeated experiments. (B) The number of proteomes identified in 3 repeated experiments. (C) Venn diagram shows the number of recognized proteins in the 3 groups and the overlap of protein recognition.

Figure 1 Identification of serum proteins in HBV patients infected with genotype B and genotype C of HBV. (A) The number of peptides identified in 3 repeated experiments. (B) The number of proteomes identified in 3 repeated experiments. (C) Venn diagram shows the number of recognized proteins in the 3 groups and the overlap of protein recognition.

In order to identify differentially expressed proteins, the relative protein expression values ​​between each HBV group and the healthy control group were compared. Based on the hierarchical cluster analysis, 53 proteins with an average expression fold change of ≥±1.5 (log2 = 0.58) in the serum of the HBV-B group were classified as differentially expressed from the serum of the healthy control group (group B vs A) (Figure 2A, Table S1). When the ratios of these 53 proteins were plotted on the heat map, it was found that 45 and 8 proteins were up-regulated and down-regulated between the serum samples of the HBV-C group and the healthy control group; in addition, the two groups of proteins were divided into different groups. Cluster (Figure 2B, Figure S3). The names of typical dysregulated proteins are listed in Table 2 and Figure S4. Then we performed GO enrichment analysis to analyze which biological processes these 53 dysregulated proteins are involved in, and found that they are divided into different clusters. The first three most abundant biological process terms are cellular processes (n = 6), metabolic processes (n = 6), and response to stimuli (n = 5) (Figure 2C and D). These results indicate that the molecular mechanism may be different between the HBV-B group and the healthy control group. Table 2 List of typical differentially expressed proteins between serum samples of HBV genotype B infected persons and C genotype infected persons Figure 2 Bioinformatics analysis of differentially expressed proteins between healthy controls and HBV B infected serum samples. (A) Representative protein Volcano map of changes in abundance (groups B and A). A total of 53 dysregulated proteins were identified with a fold change of ≥±1.5 and a p value of <0.05. (B) Hierarchical clustering of 53 dysregulated proteins (group B and group A). (C) GO analysis of 53 dysregulated proteins (group B and group A). The abscissa represents the rich classification of GO functions, divided into three categories: biological process (BP), molecular function (MF), and cellular component (CC). (D) KOG analysis of 53 dysregulated proteins (group B and group A).

Table 2 List of typical differentially expressed proteins between serum samples of genotype B infected persons and C infected persons

Figure 2 Bioinformatics analysis of differentially expressed proteins in serum samples of healthy controls and HBV genotype B infection. (A) A volcano chart representing changes in protein abundance (groups B and A). A total of 53 dysregulated proteins were identified with a fold change of ≥±1.5 and a p value of <0.05. (B) Hierarchical clustering of 53 dysregulated proteins (group B and group A). (C) GO analysis of 53 dysregulated proteins (group B and group A). The abscissa represents the rich classification of GO functions, divided into three categories: biological process (BP), molecular function (MF), and cellular component (CC). (D) KOG analysis of 53 dysregulated proteins (group B and group A).

Similarly, we compared the serum samples of HBV-C and healthy controls (group C and group A) according to the above criteria (Figure 3A, Table S2). As shown in Figures 3A and B, 59 proteins are classified as differentially expressed between HBV-C patient serum and healthy control serum (37 up-regulated and 22 down-regulated), forming different clusters. Under biological processes, GO enrichment analysis shows that dysregulated proteins are mainly related to cellular processes (n = 6), biological regulation (n = 5) and multicellular biological processes (n = 5) (Figure 3C and D). For molecular functions, most abnormally expressed proteins are mainly related to binding (n = 9). The 59 differentially expressed proteins are also classified according to their subcellular location, and each protein is assigned at least one term. The six proteins are annotated as belonging to cell parts. The other four main cell component terms related to these proteins are extracellular region (n = 6), organelle (n = 6), extracellular region (n = 6), And the organelle part (n = 6). These results also indicate that the biological processes and molecular functions of HBV patients infected with genotype B and genotype C are indeed widely differentially regulated, indicating that the differential molecular characteristics are related to the infection of the two genotypes of HBV infection. Figure 3 Bioinformatics analysis of differentially expressed proteins between healthy controls and HBV genotype C infection serum samples. (A) A volcano chart representing changes in protein abundance (group C and group A). A total of 59 dysregulated proteins were identified with a fold change of ≥±1.5 and a p value of <0.05. (B) Hierarchical clustering of 66 dysregulated proteins (group C and group A). (C) GO analysis of 59 dysregulated proteins (group C and group A). The abscissa represents the rich classification of GO functions, divided into three categories: biological process (BP), molecular function (MF), and cellular component (CC). (D) KOG analysis of 59 dysregulated proteins (group C and group A).

Figure 3 Bioinformatics analysis of differentially expressed proteins between healthy controls and HBV genotype C infection serum samples. (A) A volcano chart representing changes in protein abundance (group C and group A). A total of 59 dysregulated proteins were identified with a fold change of ≥±1.5 and a p value of <0.05. (B) Hierarchical clustering of 66 dysregulated proteins (group C and group A). (C) GO analysis of 59 dysregulated proteins (group C and group A). The abscissa represents the rich classification of GO functions, divided into three categories: biological process (BP), molecular function (MF), and cellular component (CC). (D) KOG analysis of 59 dysregulated proteins (group C and group A).

In order to further determine the pathways that are differentially regulated by HBV genotypes B and C infections, we performed an enrichment analysis based on the KEGG pathway on the differentially expressed proteins. The results show that specific signaling pathways are indeed involved in the molecular differences in host macromolecule synthesis between patients infected with HBV genotypes B and C, although some common signaling pathways have also been identified. KEGG pathway enrichment analysis showed that the differentially expressed proteins in the serum of patients in the HBV-B group were mainly involved in the regulation of complement and coagulation cascade and Staphylococcus aureus infection, while the differentially expressed proteins in the serum of patients in the HBV-C group were mainly involved in NF -κB signaling pathway, PI3K-Akt signaling pathway, focal adhesion, ECM-receptor interaction, cell adhesion, and the complement and coagulation cascade (Figures 4 and 5). Interestingly, although all of the above signaling pathways were found to be differentially regulated among patients infected with different HBV genotypes, only the "complement and coagulation cascade" was associated with genotype B and C infections. These results indicate that in HBV genotype B and C infections, the complement and coagulation cascade signaling pathways may regulate the host's innate immune response to HBV infection by regulating the differentially expressed proteins identified. Figure 4 KEGG annotations of dysregulated proteins from healthy control individuals and HBV-B (A) and healthy control individuals and HBV-C (B). Figure 5 Key signaling pathways involved in serum samples of HBV patients infected with genotype B and HBV patients infected with genotype C. ECM-receptor interaction (A) and complement and coagulation cascade (B) are derived from the enrichment analysis of dysregulated proteins based on the KEGG pathway.

Figure 4 KEGG annotations of dysregulated proteins from healthy control individuals and HBV-B (A) and healthy control individuals and HBV-C (B).

Figure 5 Key signaling pathways involved in serum samples of HBV patients infected with genotype B and HBV patients infected with genotype C. ECM-receptor interaction (A) and complement and coagulation cascade (B) are derived from the enrichment analysis of dysregulated proteins based on the KEGG pathway.

Based on the results of hierarchical clustering analysis (Figure 3) and key modulation signal pathways (Figure 5), von Willebrand factor (VWF, 1.67 times) and complement C8 β chain (C8B, 2.02 times) proteins in the HBV-C group of CHB patients HBV-B group. VWF is a large multimeric glycoprotein, mainly synthesized by endothelial cells (EC), constitutively deposited in the subendothelial extracellular matrix (ECM), and released into the plasma in the form of abnormally large multimers. VWF It plays a role in primary hemostasis mainly through mediation31,32 C8B is one of the three subunits of complement component 8 (C8), plays a key role in the formation of membrane attack complex (MAC) and cell perforation, and mediates The combination of C8 and C5b-7.33. Therefore, these factors may be important biomarkers for distinguishing patients with genotype B and C HBV infection.

The changes of VWF and C8B expression were further verified by ELISA at the protein level, using independent 108 groups of serum samples (36 cases from healthy controls, 36 cases from HBV-B group patients, 36 cases from HBV-C group patients), and each The samples are tested independently and not combined. Figure 6 shows the ELISA results of VWF and C8B expression in serum samples from individual healthy controls and patients in the HBV-B and HBV-C groups. Compared with the serum samples of patients in the HBV-B group, the levels of VWF and C8B in the patients' serum samples were significantly up-regulated (2.01 times and 2.10 times, respectively, n = 36 patients, P <0.01 for the two proteins). In group C. Figure 6 ELISA to verify selected VWF (A) and C8B (B) differentially expressed proteins in serum samples from HBV patients infected with genotype B and HBV patients infected with genotype C in the validation cohort. Data are expressed as mean SEM (n = 36, *P <0.05, **P <0.01).

Figure 6 ELISA to verify selected VWF (A) and C8B (B) differentially expressed proteins in serum samples from HBV patients infected with genotype B and HBV patients infected with genotype C in the validation cohort. Data are expressed as mean SEM (n = 36, *P <0.05, **P <0.01).

In summary, we determined the difference in VWF and C8B expression between CHB patients infected with genotype B and genotype C, which is consistent with the results obtained from DIA-based quantitative proteomics analysis (Figure 3 and Table S2). The results of the study indicate that these two proteins can be used as biomarkers to distinguish between genotype B and genotype C HBV patients; however, the underlying molecular mechanisms need to be further studied.

Although the incidence of chronic HBV infection is gradually decreasing, it is still a major public health problem worldwide due to its widespread distribution and related liver-related morbidity. 1,2 HBV genotype may be the reason for the difference in the natural history of chronic HBV infection, and therefore, plays an important role in the clinical and virological characteristics of infection, disease progression, and response to antiviral therapy. 7,34,35 Patients infected with HBV genotype C account for a large proportion of cases of severe liver disease. In addition, previous studies have confirmed that, compared with genotypes B.7, 10, and 17, genotype C is a risk factor for perinatal infection and is associated with an increased risk of serious complications, including liver cirrhosis and the development of HCC . The literature mainly focuses on the differences between genotype B and genotype C infected HBV patients, and little is known about the underlying mechanisms and the differences in specific biological pathways associated with different genotypes. To the best of our knowledge, the serum proteomics data provided in this study reports for the first time the identification of biomarkers that can be used to distinguish HBV genotype B and C infections. This study is also the first to describe the difference between HBV genotype B and genotype C infected patients at the proteomic level. These results not only confirm the results of earlier studies that HBV genotypes are related to their clinical manifestations. Compared with genotype B, infection with HBV genotype C can cause more severe hepatitis, but also provides information about the molecular relationship between HBV genotypes. New information about the difference. HBV genotypes B and C infection. These new findings may help further detailed studies related to HBV genotype-specific pathology.

In this study, we used a quantitative proteomics method based on DIA-MS to compare and analyze the whole serum proteome of the sample to gain insight into the different pathophysiology of HBV infection between genotype B and genotype C patients. We quantified a total of 1923 proteins with an FDR of <1%, which is relatively high compared to other studies that performed serum proteomics analysis. 36,37 Among the identified proteins, 63.72% are shared among all three groups, which strongly supports the stability of the workflow and the reliability of the research conclusions. Through a comprehensive analysis of the differentially expressed proteins involved in the signal pathway, significant differences in the healthy control, HBV-B and HBV-C groups at the molecular level were determined. According to the identification criteria of dysregulated proteins (fold change ≥±1.5, P <0.05), 53 differentially expressed proteins between the HBV-B group and the healthy control group were identified in the serum samples. The difference between the HBV-C group and the healthy control group was 59 differentially expressed proteins. Although all the signaling pathways related to the above-mentioned dysregulated proteins are key players in genotype B and C infection, only the complement and coagulation cascade signaling pathways are widely involved in the two control groups ( CHB patients of healthy control and HBV-B group, healthy control and HBV-C group). This finding suggests that in patients infected with HBV genotypes B and C, the complement and coagulation cascade signaling pathways may help regulate clinical features and the progression of hepatitis B disease during infection.

The complement and coagulation cascade, as the main proteolytic cascade in serum, consists of a large number of soluble plasma proteins and receptors. 38 According to reports, the complement and coagulation cascade are key mediators of the host's innate and adaptive immune system responses against pathogens, including HBV infection. These cascades are mediated through three different target recognition pathways, namely the classical pathway (CP), which is activated by antigen-antibody complexes or C-reactive protein, and the lectin pathway (LP) is triggered by any permitted surface and alternative pathways (AP ), including the direct activation of C3 and then C5. 38,39 In the case of viral infection, the activated complement and coagulation cascade are not only the process of inflammation and immune response, but also the key determinants of virus transmission, which is serious Degree 40-42 Our data support that the clinical manifestations of genotype B and C HBV infection are strongly dependent on the degree of activation or inhibition of the complement and coagulation cascade signaling pathways, which explains the involvement of this signaling pathway in the two sets of pathways.

Compared with the healthy control group and the HBV-B group, the expression of VWF in the serum samples of the HBV-C group was significantly up-regulated. VWF is synthesized in vascular endothelial cells to achieve normal hemostatic function, and is stored as abnormally large multimers in platelet alpha particles, which are secreted by thrombosis stimulation. 31,32 The size of VWF multimers indicates that they have important pathophysiological functions in the activation of complement and coagulation cascades, that is, smaller VWF multimers enhance the cleavage of C3b, while large and very large VWF (ULVWF) multimers do not. Affects C3b lysis, but promotes default complement activation. 43 In addition, C3b and ULVWF multimers are secreted by stimulated ECs and anchored on their surface to promote the activation of AP C3 convertase (C3bBb) and C5 convertase (C3bBb3b). 44,45 At the same time, it has also been reported that VWF is related to the progression of liver fibrosis in chronic hepatitis, and is negatively correlated with albumin level, prothrombin time, and platelet count through angiogenesis and cell apoptosis, which are critical to the development of HCC Important t.31,46 Consistent with these functions, the dysregulated expression of VWF in the complement and coagulation cascade can affect the susceptibility of HBV patients to complement-mediated liver fibrosis and HCC. The results of this study indicate that compared with the serum of HBV genotype B patients, the up-regulation of VWF expression may increase AP-related transcriptional activation through the formation of C3 and C5 convertases in the serum of HBV genotype C patients. Compare with health. Activated AP may play a role in promoting liver fibrosis through the assembly and activation of C3bBb and C3bBb3b, because AP activation is required for the upregulation of many proteins (such as VWF).

Compared with the serum of patients in the HBV-B group, the expression of C8B in the serum of patients in the HBV-C group was also significantly up-regulated. C8B is the main component of MAC. As an important innate immune effector, C8B forms cytotoxic pores on the surface of bacteria and enveloped viruses after complement activation. 33,47 Recent studies exploring the role of C8B in patients with HBV-related HCC have shown that even after adjusting the clinicopathological characteristics such as tumor lymph node metastasis staging, Barcelona Clinic liver cancer staging, gender, and fibrinogen beta chain (FGB) expression, high The level of C8B still contributes to favorable overall survival and recurrence-free survival. 48 The complement and coagulation cascade signaling pathway provides an effective means to target and eliminate infected cells and invading microorganisms, thereby playing an important effector function in the innate immune system. Therefore, the KEGG pathway analysis results show that in our research, many proteins related to these functions (including C8B) are dysregulated. In addition, consistent with these collective findings, our observations indicate that the upregulation of C8B and other soluble regulators of the complement and coagulation cascade pathways (including VWF) may enhance the complement and coagulation cascade signaling pathways in serum samples from genotype C Serum samples of patients with activated HBV infection relative to patients with HBV genotype B infection. This indicates that the expression levels of VWF and C8B may be potential biomarkers for distinguishing genotype B and C infections. These serum biomarkers may help diagnose HBV genotype C infection, which may be beneficial to disease prevention and control. However, the mechanism of how VWF and C8B regulate the complement and coagulation cascade during the course of the disease remains uncertain.

Overall, we analyzed the serum proteins of healthy controls and patients with HBV genotypes B and C, and identified two potential biomarkers and possible therapeutic targets for the treatment of genotype B and C HBV infection-related diseases. However, this study has several limitations. This study consisted of a relatively small sample of patients with HBV genotypes B and C. In addition, DIA-based MS analysis is performed on relatively few serum samples and only outlines differential protein expression. It is necessary to investigate the function of key dysregulated proteins through studies involving large sample sizes.

Here, we applied DIA-based MS analysis to assess the differences in serum proteome profiles between healthy controls, genotype B and genotype C HBV patients. As expected, our results clearly indicate that HBV genotypes B and C have different protein profiles and signaling pathways associated with infection. In order to verify these results, the differences in the expression levels of VWF and C8B in samples from genotype B and C infected patients were studied by ELISA. In summary, although the analysis of quantitative proteomics analysis is still mainly descriptive, the results of this study provide insight into the molecular differences between individuals infected with HBV genotypes B and C, and indicate a signal cascade with complement and coagulation. Potential applications of related disorders: VWF and C8B protein are used as biomarkers to distinguish between HBV genotype B infection and genotype C infection.

This work was supported by Zhejiang Provincial Medical Science Research Fund Project 2021KY1108, Zhejiang Provincial Natural Science Foundation Project LY20C010004, 2019 Jiaxing Medicine-Lemology Key Discipline (Supporting Project 2019-zc)-02), and the construction of the Key Laboratory of Viral Infectious Diseases in Jiaxing City project.

The authors report no conflicts of interest in this work.

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