The Role of Infected Cell Proliferation in the Clearance of Acute HBV Infection in Humans

The roles of continuous and discontinuous proliferations on hepatitis B virus infection

The proliferation of both uninfected and infected hepatocytes, as well as the recycling effects of rcDNA-containing capsids are two key mechanisms playing significant roles in the persistence and clearance of hepatitis B virus (HBV) infection. In this study, the temporal dynamics of this viral infection is investigated through two intercellular mathematical models considering proliferation of both types of hepatocytes (uninfected and infected) and recycling effects of capsids. Both models are formulated on the basis of a key finding in the existing literature: mitosis of an infected hepatocytes yields in two uninfected progenies. In the first model (defined by P-model), we examine the continuous proliferation (which occur continuously), while the second one (defined by M-model) deals with the discontinuous proliferation (happen when the concentration of liver cells decreases to less than 70% of its initial concentration). The proposed models are calibrated with the experimental data obtained from an adult chimpanzee. Results of this study suggest that when both hepatocytes proliferate with equal rate, proliferation helps the individual in a rapid recovery from the acute infection whereas in case of chronic infection, the severity of the infection increases. On the other hand, if the infected hepatocytes proliferate at a different rate that of uninfected hepatocytes, the proliferation of uninfected hepatocytes contributes to increase the infection, but the proliferation of infected hepatocytes acts to reduce the infection from the long-term perspective. The global sensitivity analysis also shows the same results. Furthermore, it is also observed that the differences between the outcomes of continuous and discontinuous proliferations are significant and noteworthy.

Disease Progression Mathematical Modeling With a Case Study on Hepatitis B Virus Infection

Chronic Hepatitis B presents a significant health and socioeconomic burden. The risk of hepatocellular carcinoma remains elevated although treatments are available. Achieving an optimal treatment regimen necessitates a deep comprehension of the dynamic relationship between the virus and its host across disease states. This tutorial elucidates essential considerations for establishing a disease modeling platform to facilitate informed decision-making in hepatitis B treatment strategies. We review several published models of varying complexity and describe the context that motivated each model's structure and assumptions. Several of the models are made available in an interactive RShiny app to demonstrate the influence of model choice and sensitivity to the choice of parameter values.

Application of MIDD to accelerate the development of anti-infectives: Current status and future perspectives

This review examines the role of model-informed drug development (MIDD) in advancing antibacterial and antiviral drug development, with an emphasis on the inclusion of host system dynamics into modeling efforts. Amidst the growing challenges of multidrug resistance and diminishing market returns, innovative methodologies are crucial for continuous drug discovery and development. The MIDD approach, with its robust capacity to integrate diverse data types, offers a promising solution. In particular, the utilization of appropriate modeling and simulation techniques for better characterization and early assessment of drug resistance are discussed. The evolution of MIDD practices across different infectious disease fields is also summarized, and compared to advancements achieved in oncology. Moving forward, the application of MIDD should expand into host system dynamics as these considerations are critical for the development of "live drugs" (e.g. chimeric antigen receptor T cells or bacteriophages) to address issues like antibiotic resistance or latent viral infections.

How robust are estimates of key parameters in standard viral dynamic models?

Mathematical models of viral infection have been developed, fitted to data, and provide insight into disease pathogenesis for multiple agents that cause chronic infection, including HIV, hepatitis C, and B virus. However, for agents that cause acute infections or during the acute stage of agents that cause chronic infections, viral load data are often collected after symptoms develop, usually around or after the peak viral load. Consequently, we frequently lack data in the initial phase of viral growth, i.e., when pre-symptomatic transmission events occur. Missing data may make estimating the time of infection, the infectious period, and parameters in viral dynamic models, such as the cell infection rate, difficult. However, having extra information, such as the average time to peak viral load, may improve the robustness of the estimation. Here, we evaluated the robustness of estimates of key model parameters when viral load data prior to the viral load peak is missing, when we know the values of some parameters and/or the time from infection to peak viral load. Although estimates of the time of infection are sensitive to the quality and amount of available data, particularly pre-peak, other parameters important in understanding disease pathogenesis, such as the loss rate of infected cells, are less sensitive. Viral infectivity and the viral production rate are key parameters affecting the robustness of data fits. Fixing their values to literature values can help estimate the remaining model parameters when pre-peak data is missing or limited. We find a lack of data in the pre-peak growth phase underestimates the time to peak viral load by several days, leading to a shorter predicted growth phase. On the other hand, knowing the time of infection (e.g., from epidemiological data) and fixing it results in good estimates of dynamical parameters even in the absence of early data. While we provide ways to approximate model parameters in the absence of early viral load data, our results also suggest that these data, when available, are needed to estimate model parameters more precisely.

Global dynamics of multiple delays within-host model for a hepatitis B virus infection of hepatocytes with immune response and drug therapy

In this paper, a mathematical model describing the hepatitis B virus (HBV) infection of hepatocytes with the intracellular HBV-DNA containing capsids, cytotoxic T-lymphocyte (CTL), antibodies including drug therapy (blocking new infection and inhibiting viral production) with two-time delays is studied. It incorporates the delay in the productively infected hepatocytes and the delay in an antigenic stimulation generating CTL. We verify the positivity and boundedness of solutions and determine the basic reproduction number. The local and global stability of three equilibrium points (infection-free, immune-free, and immune-activated) are investigated. Finally, the numerical simulations are established to show the role of these therapies in reducing viral replication and HBV infection. Our results show that the treatment by blocking new infection gives more significant results than the treatment by inhibiting viral production for infected hepatocytes. Further, both delays affect the number of infections and duration i.e. the longer the delay, the more severe the HBV infection.

A Semi-mechanistic Model to Characterize the Long-Term Dynamics of Hepatitis B Virus Markers During Treatment With Lamivudine and Pegylated Interferon

Antiviral treatments against hepatitis B virus (HBV) suppress viral replication but do not eradicate the virus, and need therefore to be taken lifelong to avoid relapse. Mathematical models can be useful to support the development of curative anti-HBV agents; however, they mostly focus on short-term HBV DNA data and neglect the complex host-pathogen interaction. This work aimed to characterize the effect of treatment with lamivudine and/or pegylated interferon (Peg-IFN) in 1,300 patients (hepatitis B envelope antigen (HBeAg)-positive and HBeAg-negative) treated for 1 year. A mathematical model was developed incorporating two populations of infected cells, namely I 1 , with a high transcriptional activity, that progressively evolve into I 2 , at a rate δ tr , representing cells with integrated HBV DNA that have a lower transcriptional activity. Parameters of the model were estimated in patients treated with lamivudine or Peg-IFN alone (N = 894), and the model was then validated in patients treated with lamivudine plus Peg-IFN (N = 436) to predict the virological response after a year of combination treatment. Lamivudine had a larger effect in blocking viral production than Peg-IFN (99.4-99.9% vs. 91.8-95.1%); however, Peg-IFN had a significant immunomodulatory effect, leading to an enhancement of the loss rates of I 1 (×1.7 in HBeAg-positive patients), I 2 (> ×7 irrespective of HBeAg status), and δ tr (×4.6 and ×2.0 in HBeAg-positive and HBeAg-negative patients, respectively). Using this model, we were able to describe the synergy of the different effects occurring during treatment with combination and predicted an effect of 99.99% on blocking viral production. This framework can therefore support the optimization of combination therapy with new anti-HBV agents.

Modelling the response to vaccine in non-human primates to define SARS-CoV-2 mechanistic correlates of protection

The definition of correlates of protection is critical for the development of next-generation SARS-CoV-2 vaccine platforms. Here, we propose a model-based approach for identifying mechanistic correlates of protection based on mathematical modelling of viral dynamics and data mining of immunological markers. The application to three different studies in non-human primates evaluating SARS-CoV-2 vaccines based on CD40-targeting, two-component spike nanoparticle and mRNA 1273 identifies and quantifies two main mechanisms that are a decrease of rate of cell infection and an increase in clearance of infected cells. Inhibition of RBD binding to ACE2 appears to be a robust mechanistic correlate of protection across the three vaccine platforms although not capturing the whole biological vaccine effect. The model shows that RBD/ACE2 binding inhibition represents a strong mechanism of protection which required significant reduction in blocking potency to effectively compromise the control of viral replication.

Mitosis of Hepatitis B virus-infected cells in vitro results in uninfected daughter cells

Background & Aims

The chronicity of HBV (and resultant liver disease) is determined by intrahepatic persistence of the HBV covalently closed circular DNA (cccDNA), an episomal form that encodes all viral transcripts. Therefore, cccDNA is a key target for new treatments, with the ultimate therapeutic aim being its complete elimination. Although established cccDNA molecules are known to be stable in resting hepatocytes, we aimed to understand their fate in dividing cells using in vitro models.

Methods

We infected HepG2-NTCP and HepaRG-NTCP cells with HBV and induced mitosis by passaging cells. We measured cccDNA copy number (by precise PCR assays) and HBV-expressing cells (by immunofluorescence) with wild-type HBV. We used reporter viruses expressing luciferase or RFP to track number of HBV-expressing cells over time after mitosis induction using luciferase assays and live imaging, respectively.

Results

In all cases, we observed dramatic reductions in cccDNA levels, HBV-positive cell numbers, and cccDNA-dependent protein expression after each round of cell mitosis. The rates of reduction were highly consistent with mathematical models of a complete cccDNA loss in (as opposed to dilution into) daughter cells.

Conclusions

Our results are concordant with previous animal models of HBV infection and show that HBV persistence can be efficiently overcome by inducing cell mitosis. These results support therapeutic approaches that induce liver turnover (e.g. immune modulators) in addition to direct-acting antiviral therapies to achieve hepatitis B cure.

Lay summary

Chronic hepatitis B affects 300 million people (killing 884,000 per year) and is incurable. To cure it, we need to clear the HBV genome from the liver. In this study, we looked at how the virus behaves after a cell divides. We found that it completely clears the virus, making 2 new uninfected cells. Our work informs new approaches to develop cures for chronic hepatitis B infections.

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HBV persists over decades in the liver, leading to chronic inflammation and serious liver disease.

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Controversy exists over the fate of viral DNA after cell mitosis, which is crucial to understanding viral persistence.

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We find here that 2 completely uninfected daughter cells are generated when infected cells undergo mitosis.

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Our results suggest that therapies that induce turnover of infected cells could facilitate the clearance of chronic HBV infection.

Modeling explains prolonged SARS-CoV-2 nasal shedding relative to lung shedding in remdesivir treated rhesus macaques

In clinical trials, remdesivir decreased recovery time in hospitalized patients with SARS- CoV-2 and prevented hospitalization when given early during infection, despite not reducing nasal viral loads. In rhesus macaques, early remdesivir prevented pneumonia and lowered lung viral loads, but viral loads increased in nasal passages after five days. We developed mathematical models to explain these results. Our model raises the hypotheses that: 1) in contrast to nasal passages viral load monotonically decreases in lungs during therapy because of infection-dependent generation of refractory cells, 2) slight reduction in lung viral loads with an imperfect agent may result in a substantial decrease in lung damage, and 3) increases in nasal viral load may occur due to a blunting of peak viral load which decreases the intensity of the innate immune response. We demonstrate that a higher potency drug could lower viral loads in nasal passages and lung.

An age-structured model of hepatitis B viral infection highlights the potential of different therapeutic strategies

Hepatitis B virus (HBV) is a global health threat, and its elimination by 2030 has been prioritised by the World Health Organisation. Here we present an age-structured model for the immune response to an HBV infection, which takes into account contributions from both cell-mediated and humoral immunity. The model has been validated using published patient data recorded during acute infection. It has been adapted to the scenarios of chronic infection, clearance of infection, and flare-ups via variation of the immune response parameters. The impacts of immune response exhaustion and non-infectious subviral particles on the immune response dynamics are analysed. A comparison of different treatment options in the context of this model reveals that drugs targeting aspects of the viral life cycle are more effective than exhaustion therapy, a form of therapy mitigating immune response exhaustion. Our results suggest that antiviral treatment is best started when viral load is declining rather than in a flare-up. The model suggests that a fast antibody production rate always leads to viral clearance, highlighting the promise of antibody therapies currently in clinical trials.

Low-level viremia in nucleoside analog-treated chronic hepatitis B patients

ABSTRACT

Low-level viremia (LLV) was defined as persistent or intermittent episodes of detectable hepatitis B virus (HBV) DNA (<2000 IU/mL, detection limit of 10 IU/mL) after 48 weeks of antiviral treatment. Effective antiviral therapies for chronic hepatitis B (CHB) patients, such as entecavir (ETV), tenofovir disoproxil fumarate (TDF), and tenofovir alafenamide (TAF), have been shown to inhibit the replication of HBV DNA and prevent liver-related complications. However, even with long-term antiviral therapy, there are still a number of patients with persistent or intermittent LLV. At present, the research on LLV to address whether adversely affect the clinical outcome is limited, and the follow-up treatment for these patients is open to question. At the same time, the mechanism of LLV is not clear. In this review, we summarize the incidence of LLV, the association between LLV and long-term outcomes, possible mechanisms, and management strategies in these patient populations.

Modeling reveals no direct role of the extent of HBV DNA integrations on the outcome of infection

Hepatitis B virus (HBV) with its high prevalence and death toll is one of the most important infectious diseases to study. Yet, there is very little progress in the development of within-host models for HBV, which has subsequently hindered our understanding of this virus. The uncertainty around the proliferation of infected hepatocytes has been studied but never in association with other important biological continuous events such as integrations and superinfections. This is despite the fact that these processes affect the diversity and composition of infected cell population in the liver and an improved understanding of the cellular composition will undoubtedly assist in strategizing against this viral infection. Here, we developed novel mathematical models that incorporate these key biological processes and analyzed them both analytically and numerically. Unaffected by the extent of integrated DNA (IDNA), the outcome of HBV infection was primarily dictated by the balance between processes generating and killing infected hepatocytes containing covalent closed circular DNA (cccDNA). The superinfection was found to be a key process in the spread of HBV infection as its exclusion could not reproduce experimentally observed composition of infected hepatocytes at peak of acute HBV infection, a stage where our model predicts that infected hepatocytes most likely carry both cccDNA and IDNA. Our analysis further suggested the existence of some form of selective advantage of infected hepatocytes containing only IDNA to explain the viral dynamics observed during antiviral treatment and the transition from peak to acute infection. Finally, the fine line between liver destruction and resolution of acute HBV infection was found to be highly influenced by the fate of cccDNA during cellular proliferation.

A delay model for persistent viral infections in replicating cells

Persistently infecting viruses remain within infected cells for a prolonged period of time without killing the cells and can reproduce via budding virus particles or passing on to daughter cells after division. The ability for populations of infected cells to be long-lived and replicate viral progeny through cell division may be critical for virus survival in examples such as HIV latent reservoirs, tumor oncolytic virotherapy, and non-virulent phages in microbial hosts. We consider a model for persistent viral infection within a replicating cell population with time delay in the eclipse stage prior to infected cell replicative form. We obtain reproduction numbers that provide criteria for the existence and stability of the equilibria of the system and provide bifurcation diagrams illustrating transcritical (backward and forward), saddle-node, and Hopf bifurcations, and provide evidence of homoclinic bifurcations and a Bogdanov-Takens bifurcation. We investigate the possibility of long term survival of the infection (represented by chronically infected cells and free virus) in the cell population by using the mathematical concept of robust uniform persistence. Using numerical continuation software with parameter values estimated from phage-microbe systems, we obtain two parameter bifurcation diagrams that divide parameter space into regions with different dynamical outcomes. We thus investigate how varying different parameters, including how the time spent in the eclipse phase, can influence whether or not the virus survives.

Mechanistic Modeling of SARS-CoV-2 and Other Infectious Diseases and the Effects of Therapeutics

Modern viral kinetic modeling and its application to therapeutics is a field that attracted the attention of the medical, pharmaceutical, and modeling communities during the early days of the AIDS epidemic. Its successes led to applications of modeling methods not only to HIV but a plethora of other viruses, such as hepatitis C virus (HCV), hepatitis B virus and cytomegalovirus, which along with HIV cause chronic diseases, and viruses such as influenza, respiratory syncytial virus, West Nile virus, Zika virus, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which generally cause acute infections. Here we first review the historical development of mathematical models to understand HIV and HCV infections and the effects of treatment by fitting the models to clinical data. We then focus on recent efforts and contributions of applying these models towards understanding SARS-CoV-2 infection and highlight outstanding questions where modeling can provide crucial insights and help to optimize nonpharmaceutical and pharmaceutical interventions of the coronavirus disease 2019 (COVID-19) pandemic. The review is written from our personal perspective emphasizing the power of simple target cell limited models that provided important insights and then their evolution into more complex models that captured more of the virology and immunology. To quote Albert Einstein, "Everything should be made as simple as possible, but not simpler," and this idea underlies the modeling we describe below.

Understanding the antiviral effects of RNAi-based therapy in HBeAg-positive chronic hepatitis B infection

The RNA interference (RNAi) drug ARC-520 was shown to be effective in reducing serum hepatitis B virus (HBV) DNA, hepatitis B e antigen (HBeAg) and hepatitis B surface antigen (HBsAg) in HBeAg-positive patients treated with a single dose of ARC-520 and daily nucleosidic analogue (entecavir). To provide insights into HBV dynamics under ARC-520 treatment and its efficacy in blocking HBV DNA, HBsAg, and HBeAg production we developed a multi-compartmental pharmacokinetic-pharamacodynamic model and calibrated it with frequent measured HBV kinetic data. We showed that the time-dependent single dose ARC-520 efficacies in blocking HBsAg and HBeAg are more than 96% effective around day 1, and slowly wane to 50% in 1-4 months. The combined single dose ARC-520 and entecavir effect on HBV DNA was constant over time, with efficacy of more than 99.8%. The observed continuous HBV DNA decline is entecavir mediated, the strong but transient HBsAg and HBeAg decays are ARC-520 mediated. The modeling framework may help assess ongoing RNAi drug development for hepatitis B virus infection.

Estimating hepatitis B virus cccDNA persistence in chronic infection

Hepatitis B virus (HBV) infection is a major global health problem with over 240 million infected individuals at risk of developing progressive liver disease and hepatocellular carcinoma. HBV is an enveloped DNA virus that establishes its genome as an episomal, covalently closed circular DNA (cccDNA) in the nucleus of infected hepatocytes. Currently, available standard-of-care treatments for chronic hepatitis B (CHB) include nucleos(t)ide analogues (NAs) that suppress HBV replication but do not target the cccDNA and hence rarely cure infection. There is considerable interest in determining the lifespan of cccDNA molecules to design and evaluate new curative treatments. We took a novel approach to this problem by developing a new mathematical framework to model changes in evolutionary rates during infection which, combined with previously determined within-host evolutionary rates of HBV, we used to determine the lifespan of cccDNA. We estimate that during HBe-antigen positive (HBeAgPOS) infection the cccDNA lifespan is 61 (36-236) days, whereas during the HBeAgNEG phase of infection it is only 26 (16-81) days. We found that cccDNA replicative capacity declined by an order of magnitude between HBeAgPOS and HBeAgNEG phases of infection. Our estimated lifespan of cccDNA is too short to explain the long durations of chronic infection observed in patients on NA treatment, suggesting that either a sub-population of long-lived hepatocytes harbouring cccDNA molecules persists during therapy, or that NA therapy does not suppress all viral replication. These results provide a greater understanding of the biology of the cccDNA reservoir and can aid the development of new curative therapeutic strategies for treating CHB.

Efficient Methods for Parameter Estimation of Ordinary and Partial Differential Equation Models of Viral Hepatitis Kinetics

Parameter estimation in mathematical models that are based on differential equations is known to be of fundamental importance. For sophisticated models such as age-structured models that simulate biological agents, parameter estimation that addresses all cases of data points available presents a formidable challenge and efficiency considerations need to be employed in order for the method to become practical. In the case of age-structured models of viral hepatitis dynamics under antiviral treatment that deal with partial differential equations, a fully numerical parameter estimation method was developed that does not require an analytical approximation of the solution to the multiscale model equations, avoiding the necessity to derive the long-term approximation for each model. However, the method is considerably slow because of precision problems in estimating derivatives with respect to the parameters near their boundary values, making it almost impractical for general use. In order to overcome this limitation, two steps have been taken that significantly reduce the running time by orders of magnitude and thereby lead to a practical method. First, constrained optimization is used, letting the user add constraints relating to the boundary values of each parameter before the method is executed. Second, optimization is performed by derivative-free methods, eliminating the need to evaluate expensive numerical derivative approximations. The newly efficient methods that were developed as a result of the above approach are described for hepatitis C virus kinetic models during antiviral therapy. Illustrations are provided using a user-friendly simulator that incorporates the efficient methods for both the ordinary and partial differential equation models.

Stochastic dynamics in a time-delayed model for autoimmunity

In this paper we study interactions between stochasticity and time delays in the dynamics of immune response to viral infections, with particular interest in the onset and development of autoimmune response. Starting with a deterministic time-delayed model of immune response to infection, which includes cytokines and T cells with different activation thresholds, we derive an exact delayed chemical master equation for the probability density. We use system size expansion and linear noise approximation to explore how variance and coherence of stochastic oscillations depend on parameters, and to show that stochastic oscillations become more regular when regulatory T cells become more effective at clearing autoreactive T cells. Reformulating the model as an Itô stochastic delay differential equation, we perform numerical simulations to illustrate the dynamics of the model and associated probability distributions in different parameter regimes. The results suggest that even in cases where the deterministic model has stable steady states, in individual stochastic realisations, the model can exhibit sustained stochastic oscillations, whose variance increases as one gets closer to the deterministic stability boundary. Furthermore, in the regime of bi-stability, whereas deterministically the system would approach one of the steady states (or periodic solutions) depending on the initial conditions, due to the presence of stochasticity, it is now possible for the system to reach both of those dynamical states with certain probability. Biological significance of this result lies in highlighting the fact that since normally in a laboratory or clinical setting one would observe a single individual realisation of the course of the disease, even for all parameters characterising the immune system and the strength of infection being the same, there is a proportion of cases where a spontaneous recovery can be observed, and similarly, where a disease can develop in a situation that otherwise would result in a normal disease clearance.

Within-host mathematical models of hepatitis B virus infection: Past, present, and future

Mathematical modeling has been instrumental in enhancing our understanding of the viral dynamics of hepatitis B virus (HBV) infection. We give a primer on HBV infection in humans and a brief overview of the development of within-host mathematical models of HBV infection. In the last decade, models have advanced from considering chronic HBV infections under therapy to the pathogenesis of infection. We also summarize estimates of key viral dynamic parameters that have varied greatly among studies, and show that they impact model predictions. Future directions for mathematical modeling of HBV infection are proposed to better understand emerging therapies, the HBV life cycle, predicting cure, and the mechanisms involved in the immune response to HBV infection.

Modeling the dynamics of hepatitis B infection, immunity, and drug therapy

Hepatitis B virus infection is the cause of liver diseases such as cirrhosis and liver cancer. Understanding the host-virus mechanisms that mediate virus pathogenesis can help design better preventive measures for disease control. Mathematical models have been used alongside experimental data to provide insight into the role of immune responses during the acute and chronic hepatitis B infections as well as virus dynamics following administration of combined drug therapy. In this paper, we review several modeling studies on virus-host interactions during acute infection, the virus-host characteristics responsible for transition to chronic disease, and the efficacy and optimal control measures of drug therapy. We conclude by presenting our opinion on the future directions of the field.

A dual role for SAMHD1 in regulating HBV cccDNA and RT-dependent particle genesis

Chronic hepatitis B is one of the world's unconquered diseases with more than 240 million infected subjects at risk of developing liver disease and hepatocellular carcinoma. Hepatitis B virus reverse transcribes pre-genomic RNA to relaxed circular DNA (rcDNA) that comprises the infectious particle. To establish infection of a naïve target cell, the newly imported rcDNA is repaired by host enzymes to generate covalently closed circular DNA (cccDNA), which forms the transcriptional template for viral replication. SAMHD1 is a component of the innate immune system that regulates deoxyribonucleoside triphosphate levels required for host and viral DNA synthesis. Here, we show a positive role for SAMHD1 in regulating cccDNA formation, where KO of SAMHD1 significantly reduces cccDNA levels that was reversed by expressing wild-type but not a mutated SAMHD1 lacking the nuclear localization signal. The limited pool of cccDNA in infected Samhd1 KO cells is transcriptionally active, and we observed a 10-fold increase in newly synthesized rcDNA-containing particles, demonstrating a dual role for SAMHD1 to both facilitate cccDNA genesis and to restrict reverse transcriptase-dependent particle genesis.

The dynamics of integration, viral suppression and cell-cell transmission in the development of occult Hepatitis B virus infection

BACKGROUND

Out of several phases of HBV infection, the least understood phase is occult hepatitis B virus infection. The paucity of data due to non-availability of biological tissues and the prerequisite of ultra-sensitive assays for the detection of occult hepatitis B virus infection prompted us to utilize mathematical modeling in determining mechanisms that lead to occult hepatitis B virus infection and characteristics of HBV infection during occult hepatitis B virus infection.

METHODS

We proposed two mathematical models (M1 and M2), considering two different phenomenon for episomal maintenance and accumulation of covalently closed circular DNA (cccDNA) in infected hepatocytes: (i) M1 - recirculation of the relaxed circular DNA/double-stranded linear DNA from cytoplasm to the nucleus, and (ii) M2 - reinfection of infected hepatocytes with virions. We further incorporated the dynamics of integrated Hepatitis B virus DNA (iHBV) to investigate its role in the development of occult hepatitis B virus infection.

RESULTS

The analysis showed that the main mechanism for the spread of infection during occult hepatitis B virus infection is cell-to-cell transmission and not cell-free virus transmission. A significant viral suppression (of at least 99% from its peak production values) was essential but not sufficient in the development of occult hepatitis B virus infection under M1; however under M2, the viral suppression was neither sufficient nor essential as the inhibition of the production of HBsAg without viral suppression can also explain the development of occult hepatitis B virus infection. Our analysis also revealed that occult hepatitis B virus infection seropositive cases are more likely to progress into liver cirrhosis compared to occult hepatitis B virus infection seronegative cases. The iHBV was found to be mostly silent (by either being absent or non-productive for HBsAg) during occult hepatitis B virus infection.

CONCLUSION

The viral suppression is neither essential nor sufficient to explain the development of occult hepatitis B virus infection on its own. Not only the viral suppression but the inhibition -of the production and the export of HBsAg from cccDNA and iHBV also plays an important role in the development of occult hepatitis B virus infection. This is the first study, which incorporates the dynamics of iHBV and shows that HBV primarily spreads via cell-cell transmission during occult hepatitis B virus infection.