Alteration of the Immune Microenvironment in HBsAg and HBeAg Dual-Positive Pregnant Women Presenting a High HBV Viral Load

Hepatitis B virus serum RNA transcript isoform composition and proportion in chronic hepatitis B patients by nanopore long-read sequencing

Introduction

Serum hepatitis B virus (HBV) RNA is a promising new biomarker to manage and predict clinical outcomes of chronic hepatitis B (CHB) infection. However, the HBV serum transcriptome within encapsidated particles, which is the biomarker analyte measured in serum, remains poorly characterized. This study aimed to evaluate serum HBV RNA transcript composition and proportionality by PCR-cDNA nanopore sequencing of samples from CHB patients having varied HBV genotype (gt, A to F) and HBeAg status.

Methods

Longitudinal specimens from 3 individuals during and following pregnancy (approximately 7 months between time points) were also investigated. HBV RNA extracted from 16 serum samples obtained from 13 patients (73.3% female, 84.6% Asian) was sequenced and serum HBV RNA isoform detection and quantification were performed using three bioinformatic workflows; FLAIR, RATTLE, and a GraphMap-based workflow within the Galaxy application. A spike-in RNA variant (SIRV) control mix was used to assess run quality and coverage. The proportionality of transcript isoforms was based on total HBV reads determined by each workflow.

Results

All chosen isoform detection workflows showed high agreement in transcript proportionality and composition for most samples. HBV pregenomic RNA (pgRNA) was the most frequently observed transcript isoform (93.8% of patient samples), while other detected transcripts included pgRNA spliced variants, 3' truncated variants and HBx mRNA, depending on the isoform detection method. Spliced variants of pgRNA were primarily observed in HBV gtB, C, E, or F-infected patients, with the Sp1 spliced variant detected most frequently. Twelve other pgRNA spliced variant transcripts were identified, including 3 previously unidentified transcripts, although spliced isoform identification was very dependent on the workflow used to analyze sequence data. Longitudinal sampling among pregnant and post-partum antiviral-treated individuals showed increasing proportions of 3' truncated pgRNA variants over time.

Conclusions

This study demonstrated long-read sequencing as a promising tool for the characterization of the serum HBV transcriptome. However, further studies are needed to better understand how serum HBV RNA isoform type and proportion are linked to CHB disease progression and antiviral treatment response.

Identification of Gene Markers Associated with COVID-19 Severity and Recovery in Different Immune Cell Subtypes

As COVID-19 develops, dynamic changes occur in the patient's immune system. Changes in molecular levels in different immune cells can reflect the course of COVID-19. This study aims to uncover the molecular characteristics of different immune cell subpopulations at different stages of COVID-19. We designed a machine learning workflow to analyze scRNA-seq data of three immune cell types (B, T, and myeloid cells) in four levels of COVID-19 severity/outcome. The datasets for three cell types included 403,700 B-cell, 634,595 T-cell, and 346,547 myeloid cell samples. Each cell subtype was divided into four groups, control, convalescence, progression mild/moderate, and progression severe/critical, and each immune cell contained 27,943 gene features. A feature analysis procedure was applied to the data of each cell type. Irrelevant features were first excluded according to their relevance to the target variable measured by mutual information. Then, four ranking algorithms (last absolute shrinkage and selection operator, light gradient boosting machine, Monte Carlo feature selection, and max-relevance and min-redundancy) were adopted to analyze the remaining features, resulting in four feature lists. These lists were fed into the incremental feature selection, incorporating three classification algorithms (decision tree, k-nearest neighbor, and random forest) to extract key gene features and construct classifiers with superior performance. The results confirmed that genes such as PFN1, RPS26, and FTH1 played important roles in SARS-CoV-2 infection. These findings provide a useful reference for the understanding of the ongoing effect of COVID-19 development on the immune system.

Effects of hepatitis B virus infection and strategies for preventing mother-to-child transmission on maternal and fetal T-cell immunity

One of the most common routes of chronic hepatitis B virus (HBV) infection is mother-to-child transmission (MTCT). Approximately 6.4 million children under the age of five have chronic HBV infections worldwide. HBV DNA high level, HBeAg positivity, placental barrier failure, and immaturity of the fetal immune are the possible causes of chronic HBV infection. The passive-active immune program for children, which consists of the hepatitis B vaccine and hepatitis B immunoglobulin, and antiviral therapy for pregnant women who have a high HBV DNA load (greater than 2 × 10⁵ IU/ml), are currently two of the most important ways to prevent the transmission of HBV from mother to child. Unfortunately, some infants still have chronic HBV infections. Some studies have also found that some supplementation during pregnancy can increase cytokine levels and then affect the level of HBsAb in infants. For example, IL-4 can mediate the beneficial effect on infants' HBsAb levels when maternal folic acid supplementation. In addition, new research has indicated that HBV infection in the mother may also be linked to unfavorable outcomes such as gestational diabetes mellitus, intrahepatic cholestasis of pregnancy, and premature rupture of membranes. The changes in the immune environment during pregnancy and the hepatotropic nature of HBV may be the main reasons for the adverse maternal outcomes. It is interesting to note that after delivery, the women who had a chronic HBV infection may spontaneously achieve HBeAg seroconversion and HBsAg seroclearance. The maternal and fetal T-cell immunity in HBV infection is important because adaptive immune responses, especially virus-specific CD8 T-cell responses, are largely responsible for viral clearance and disease pathogenesis during HBV infection. Meanwhile, HBV humoral and T-cell responses are important for the durability of protection after fetal vaccination. This article reviews the literature on immunological characteristics of chronic HBV-infected patients during pregnancy and postpartum, blocking mother-to-child transmissions and related immune mechanisms, hoping to provide new insights for the prevention of HBV MTCT and antiviral intervention during pregnancy and postpartum.

Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method

Notably, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a tight relationship with the immune system. Human resistance to COVID-19 infection comprises two stages. The first stage is immune defense, while the second stage is extensive inflammation. This process is further divided into innate and adaptive immunity during the immune defense phase. These two stages involve various immune cells, including CD4⁺ T cells, CD8⁺ T cells, monocytes, dendritic cells, B cells, and natural killer cells. Various immune cells are involved and make up the complex and unique immune system response to COVID-19, providing characteristics that set it apart from other respiratory infectious diseases. In the present study, we identified cell markers for differentiating COVID-19 from common inflammatory responses, non-COVID-19 severe respiratory diseases, and healthy populations based on single-cell profiling of the gene expression of six immune cell types by using Boruta and mRMR feature selection methods. Some features such as IFI44L in B cells, S100A8 in monocytes, and NCR2 in natural killer cells are involved in the innate immune response of COVID-19. Other features such as ZFP36L2 in CD4⁺ T cells can regulate the inflammatory process of COVID-19. Subsequently, the IFS method was used to determine the best feature subsets and classifiers in the six immune cell types for two classification algorithms. Furthermore, we established the quantitative rules used to distinguish the disease status. The results of this study can provide theoretical support for a more in-depth investigation of COVID-19 pathogenesis and intervention strategies.