Replication vesicles are load- and choke-points in the hepatitis C virus lifecycle

A detailed kinetic model of Eastern equine encephalitis virus replication in a susceptible host cell

Eastern equine encephalitis virus (EEEV) is an arthropod-borne, positive-sense RNA alphavirus posing a substantial threat to public health. Unlike similar viruses such as SARS-CoV-2, EEEV replicates efficiently in neurons, producing progeny viral particles as soon as 3-4 hours post-infection. EEEV infection, which can cause severe encephalitis with a human mortality rate surpassing 30%, has no licensed, targeted therapies, leaving patients to rely on supportive care. Although the general characteristics of EEEV infection within the host cell are well-studied, it remains unclear how these interactions lead to rapid production of progeny viral particles, limiting development of antiviral therapies. Here, we present a novel rule-based model that describes attachment, entry, uncoating, replication, assembly, and export of both infectious virions and virus-like particles within mammalian cells. Additionally, it quantitatively characterizes host ribosome activity in EEEV replication via a model parameter defining ribosome density on viral RNA. To calibrate the model, we performed experiments to quantify viral RNA, protein, and infectious particle production during acute infection. We used Bayesian inference to calibrate the model, discovering in the process that an additional constraint was required to ensure consistency with previous experimental observations of a high ratio between the amounts of full-length positive-sense viral genome and negative-sense template strand. Overall, the model recapitulates the experimental data and predicts that EEEV rapidly concentrates host ribosomes densely on viral RNA. Dense packing of host ribosomes was determined to be critical to establishing the characteristic positive to negative RNA strand ratio because of its role in governing the kinetics of transcription. Sensitivity analysis identified viral transcription as the critical step for infectious particle production, making it a potential target for future therapeutic development.

Assessing the performance of QSP models: biology as the driver for validation

Validation of a quantitative model is a critical step in establishing confidence in the model's suitability for whatever analysis it was designed. While processes for validation are well-established in the statistical sciences, the field of quantitative systems pharmacology (QSP) has taken a more piecemeal approach to defining and demonstrating validation. Although classical statistical methods can be used in a QSP context, proper validation of a mechanistic systems model requires a more nuanced approach to what precisely is being validated, and what role said validation plays in the larger context of the analysis. In this review, we summarize current thoughts of QSP validation in the scientific community, contrast the aims of statistical validation from several contexts (including inference, pharmacometrics analysis, and machine learning) with the challenges faced in QSP analysis, and use examples from published QSP models to define different stages or levels of validation, any of which may be sufficient depending on the context at hand.

Intracellular "In Silico Microscopes"-Comprehensive 3D Spatio-Temporal Virus Replication Model Simulations

Despite their small and simple structure compared with their hosts, virus particles can cause severe harm and even mortality in highly evolved species such as humans. A comprehensive quantitative biophysical understanding of intracellular virus replication mechanisms could aid in preparing for future virus pandemics. By elucidating the relationship between the form and function of intracellular structures from the host cell and viral components, it is possible to identify possible targets for direct antiviral agents and potent vaccines. Biophysical investigations into the spatio-temporal dynamics of intracellular virus replication have thus far been limited. This study introduces a framework to enable simulations of these dynamics using partial differential equation (PDE) models, which are evaluated using advanced numerical mathematical methods on leading supercomputers. In particular, this study presents a model of the replication cycle of a specific RNA virus, the hepatitis C virus. The diffusion-reaction model mimics the interplay of the major components of the viral replication cycle, including non structural viral proteins, viral genomic RNA, and a generic host factor. Technically, surface partial differential equations (sufPDEs) are coupled on the 3D embedded 2D endoplasmic reticulum manifold with partial differential equations (PDEs) in the 3D membranous web and cytosol volume. The membranous web serves as a viral replication factory and is formed on the endoplasmic reticulum after infection and in the presence of nonstructural proteins. The coupled sufPDE/PDE model was evaluated using realistic cell geometries based on experimental data. The simulations incorporate the effects of non structural viral proteins, which are restricted to the endoplasmic reticulum surface, with effects appearing in the volume, such as host factor supply from the cytosol and membranous web dynamics. Because the spatial diffusion properties of genomic viral RNA are not yet fully understood, the model allows for viral RNA movement on the endoplasmic reticulum as well as within the cytosol. Visualizing the simulated intracellular viral replication dynamics provides insights similar to those obtained by microscopy, complementing data from in vitro/in vivo viral replication experiments. The output data demonstrate quantitative consistence with the experimental findings, prompting further advanced experimental studies to validate the model and refine our quantitative biophysical understanding.

High-resolution kinetic characterization of the RIG-I-signaling pathway and the antiviral response

RIG-I recognizes viral dsRNA and activates a cell-autonomous antiviral response. Upon stimulation, it triggers a signaling cascade leading to the production of type I and III IFNs. IFNs are secreted and signal to elicit the expression of IFN-stimulated genes, establishing an antiviral state of the cell. The topology of this pathway has been studied intensively, however, its exact dynamics are less understood. Here, we employed electroporation to synchronously activate RIG-I, enabling us to characterize cell-intrinsic innate immune signaling at a high temporal resolution. Employing IFNAR1/IFNLR-deficient cells, we could differentiate primary RIG-I signaling from secondary signaling downstream of the IFN receptors. Based on these data, we developed a comprehensive mathematical model capable of simulating signaling downstream of dsRNA recognition by RIG-I and the feedback and signal amplification by IFN. We further investigated the impact of viral antagonists on signaling dynamics. Our work provides a comprehensive insight into the signaling events that occur early upon virus infection and opens new avenues to study and disentangle the complexity of the host-virus interface.

Transcriptomics-inferred dynamics of SARS-CoV-2 interactions with host epithelial cells

Virus-host interactions can reveal potentially effective and selective therapeutic targets for treating infection. Here, we performed an integrated analysis of the dynamics of virus replication and the host cell transcriptional response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using human Caco-2 colon cancer cells as a model. Time-resolved RNA sequencing revealed that, upon infection, cells immediately transcriptionally activated genes associated with inflammatory pathways that mediate the antiviral response, which was followed by an increase in the expression of genes involved in ribosome and mitochondria function, thus suggesting rapid alterations in protein production and cellular energy supply. At later stages, between 24 and 48 hours after infection, the expression of genes involved in metabolic processes-in particular, those related to xenobiotic metabolism-was decreased. Mathematical modeling incorporating SARS-CoV-2 replication suggested that SARS-CoV-2 proteins inhibited the host antiviral response and that virus transcripts exceeded the translation capacity of the host cells. Targeting kinase-dependent pathways that exhibited increases in transcription in host cells was as effective as a virus-targeted inhibitor at repressing viral replication. Our findings in this model system delineate a sequence of SARS-CoV-2 virus-host interactions that may facilitate the identification of druggable host pathways to suppress infection.

Mathematical modeling of plus-strand RNA virus replication to identify broad-spectrum antiviral treatment strategies

Plus-strand RNA viruses are the largest group of viruses. Many are human pathogens that inflict a socio-economic burden. Interestingly, plus-strand RNA viruses share remarkable similarities in their replication. A hallmark of plus-strand RNA viruses is the remodeling of intracellular membranes to establish replication organelles (so-called "replication factories"), which provide a protected environment for the replicase complex, consisting of the viral genome and proteins necessary for viral RNA synthesis. In the current study, we investigate pan-viral similarities and virus-specific differences in the life cycle of this highly relevant group of viruses. We first measured the kinetics of viral RNA, viral protein, and infectious virus particle production of hepatitis C virus (HCV), dengue virus (DENV), and coxsackievirus B3 (CVB3) in the immuno-compromised Huh7 cell line and thus without perturbations by an intrinsic immune response. Based on these measurements, we developed a detailed mathematical model of the replication of HCV, DENV, and CVB3 and showed that only small virus-specific changes in the model were necessary to describe the in vitro dynamics of the different viruses. Our model correctly predicted virus-specific mechanisms such as host cell translation shut off and different kinetics of replication organelles. Further, our model suggests that the ability to suppress or shut down host cell mRNA translation may be a key factor for in vitro replication efficiency, which may determine acute self-limited or chronic infection. We further analyzed potential broad-spectrum antiviral treatment options in silico and found that targeting viral RNA translation, such as polyprotein cleavage and viral RNA synthesis, may be the most promising drug targets for all plus-strand RNA viruses. Moreover, we found that targeting only the formation of replicase complexes did not stop the in vitro viral replication early in infection, while inhibiting intracellular trafficking processes may even lead to amplified viral growth.

Mechanistic modeling of viral particle production

Viral systems such as wild-type viruses, viral vectors, and virus-like particles are essential components of modern biotechnology and medicine. Despite their importance, the commercial-scale production of viral systems remains highly inefficient for multiple reasons. Computational strategies are a promising avenue for improving process development, optimization, and control, but require a mathematical description of the system. This article reviews mechanistic modeling strategies for the production of viral particles, both at the cellular and bioreactor scales. In many cases, techniques and models from adjacent fields such as epidemiology and wild-type viral infection kinetics can be adapted to construct a suitable process model. These process models can then be employed for various purposes such as in-silico testing of novel process operating strategies and/or advanced process control.

Mathematical modeling of plus-strand RNA virus replication to identify broad-spectrum antiviral treatment strategies

Plus-strand RNA viruses are the largest group of viruses. Many are human pathogens that inflict a socio-economic burden. Interestingly, plus-strand RNA viruses share remarkable similarities in their replication. A hallmark of plus-strand RNA viruses is the remodeling of intracellular membranes to establish replication organelles (so-called "replication factories"), which provide a protected environment for the replicase complex, consisting of the viral genome and proteins necessary for viral RNA synthesis. In the current study, we investigate pan-viral similarities and virus-specific differences in the life cycle of this highly relevant group of viruses. We first measured the kinetics of viral RNA, viral protein, and infectious virus particle production of hepatitis C virus (HCV), dengue virus (DENV), and coxsackievirus B3 (CVB3) in the immuno-compromised Huh7 cell line and thus without perturbations by an intrinsic immune response. Based on these measurements, we developed a detailed mathematical model of the replication of HCV, DENV, and CVB3 and show that only small virus-specific changes in the model were necessary to describe the in vitro dynamics of the different viruses. Our model correctly predicted virus-specific mechanisms such as host cell translation shut off and different kinetics of replication organelles. Further, our model suggests that the ability to suppress or shut down host cell mRNA translation may be a key factor for in vitro replication efficiency which may determine acute self-limited or chronic infection. We further analyzed potential broad-spectrum antiviral treatment options in silico and found that targeting viral RNA translation, especially polyprotein cleavage, and viral RNA synthesis may be the most promising drug targets for all plus-strand RNA viruses. Moreover, we found that targeting only the formation of replicase complexes did not stop the viral replication in vitro early in infection, while inhibiting intracellular trafficking processes may even lead to amplified viral growth.

Author summary

Plus-strand RNA viruses comprise a large group of related and medically relevant viruses. The current global pandemic of COVID-19 caused by the SARS-coronavirus-2 as well as the constant spread of diseases such as dengue and chikungunya fever show the necessity of a comprehensive and precise analysis of plus-strand RNA virus infections. Plus-strand RNA viruses share similarities in their life cycle. To understand their within-host replication strategies, we developed a mathematical model that studies pan-viral similarities and virus-specific differences of three plus-strand RNA viruses, namely hepatitis C, dengue, and coxsackievirus. By fitting our model to in vitro data, we found that only small virus-specific variations in the model were required to describe the dynamics of all three viruses. Furthermore, our model predicted that ribosomes involved in viral RNA translation seem to be a key player in plus-strand RNA replication efficiency, which may determine acute or chronic infection outcome. Furthermore, our in-silico drug treatment analysis suggests that targeting viral proteases involved in polyprotein cleavage, in combination with viral RNA replication, may represent promising drug targets with broad-spectrum antiviral activity.

Mechanisms of phosphatidylserine influence on viral production: A computational model of Ebola virus matrix protein assembly

Ebola virus (EBOV) infections continue to pose a global public health threat, with high mortality rates and sporadic outbreaks in Central and Western Africa. A quantitative understanding of the key processes driving EBOV assembly and budding could provide valuable insights to inform drug development. Here, we use a computational model to evaluate EBOV matrix assembly. Our model focuses on the assembly kinetics of VP40, the matrix protein in EBOV, and its interaction with phosphatidylserine (PS) in the host cell membrane. It has been shown that mammalian cells transfected with VP40-expressing plasmids are capable of producing virus-like particles (VLPs) that closely resemble EBOV virions. Previous studies have also shown that PS levels in the host cell membrane affects VP40 association with the plasma membrane inner leaflet and that lower membrane PS levels result in lower VLP production. Our computational findings indicate that PS may also have a direct influence on VP40 VLP assembly and budding, where a higher PS level will result in a higher VLP budding rate and filament dissociation rate. Our results further suggest that the assembly of VP40 filaments follow the nucleation-elongation theory, where initialization and oligomerization of VP40 are two distinct steps in the assembly process. Our findings advance the current understanding of VP40 VLP formation by identifying new possible mechanisms of PS influence on VP40 assembly. We propose that these mechanisms could inform treatment strategies targeting PS alone or in combination with other VP40 assembly steps.

Identification of phytochemicals from Eclipta alba and assess their potentiality against Hepatitis C virus envelope glycoprotein: virtual screening, docking, and molecular dynamics simulation study

Hepatitis C virus has a major role in spreading chronic liver disease and hepatocellular carcinoma. Factors such as high costs, pharmacological side effects, and the development of drug resistance strains require the development of new and potentially effective antiviral to treat the various stages of Hepatitis C. Bioactive chemicals have been extracted from medicinal plants and are utilized by humans for the goal of maintaining a healthy lifestyle. The goal of this work is to recognize phytochemicals from Eclipta alba and assess their potentiality activity against the hepatitis C virus envelope glycoprotein using in silico approaches. Phytochemicals from Eclipta alba were virtually screened by Auto dock raccoon and 12 compounds were selected for molecular docking to probe the active binding site. The top two compounds based on the binding score like ecliptalbine and oleanolic acid with HCV E2 glycoprotein exhibit binding energy -8.88 and -8.02 kcal/mol, respectively. The chemicals' usefulness was reinforced by positive pharmacokinetic data. The phytocompounds were identified as potent HCV inhibitors based on the drug likeness and ADMET properties. Both ecliptalbine and oleanolic acid underwent molecular dynamics simulations to determine features such as RMSD, RMSF, SASA, hydrogen-bond number, and MM-PBSA-based binding free energy. From the molecular docking and molecular dynamics simulation study revealed that oleanolic acid obtained from Eclipta alba can be used as inhibitors against Hepatitis C. The identified inhibitor from our study will be study in vitro and in vivo studies to check their efficacy against Hepatitis C.Communicated by Ramaswamy H. Sarma.

Life cycle process dependencies of positive-sense RNA viruses suggest strategies for inhibiting productive cellular infection

Life cycle processes of positive-strand (+)RNA viruses are broadly conserved across families, yet they employ different strategies to grow in the cell. Using a generalized dynamical model for intracellular (+)RNA virus growth, we decipher these life cycle determinants and their dependencies for several viruses and parse the effects of viral mutations, drugs and host cell permissivity. We show that poliovirus employs rapid replication and virus assembly, whereas the Japanese encephalitis virus leverages its higher rate of translation and efficient cellular reorganization compared to the hepatitis C virus. Stochastic simulations demonstrate infection extinction if all seeding (inoculating) viral RNA degrade before establishing robust replication critical for infection. The probability of this productive cellular infection, 'cellular infectivity', is affected by virus-host processes and defined by early life cycle events and viral seeding. An increase in cytoplasmic RNA degradation and delay in vesicular compartment formation reduces infectivity, more so when combined. Synergy among these parameters in limiting (+)RNA virus infection as predicted by our model suggests new avenues for inhibiting infections by targeting the early life cycle bottlenecks.

Dengue virus is sensitive to inhibition prior to productive replication

Uncovering vulnerable steps in the life cycle of viruses supports the rational design of antiviral treatments. However, information on viral replication dynamics obtained from traditional bulk assays with host cell populations is inherently limited as the data represent averages over a multitude of unsynchronized replication cycles. Here, we use time-lapse imaging of virus replication in thousands of single cells, combined with computational inference, to identify rate-limiting steps for dengue virus (DENV), a widespread human pathogen. Comparing wild-type DENV with a vaccine candidate mutant, we show that the viral spread in the mutant is greatly attenuated by delayed onset of productive replication, whereas wild-type and mutant virus have identical replication rates. Single-cell analysis done after applying the broad-spectrum antiviral drug, ribavirin, at clinically relevant concentrations revealed the same mechanism of attenuating viral spread. We conclude that the initial steps of infection, rather than the rate of established replication, are quantitatively limiting DENV spread.

Modeling the complete kinetics of coxsackievirus B3 reveals human determinants of host-cell feedback

Complete kinetic models are pervasive in chemistry but lacking in biological systems. We encoded the complete kinetics of infection for coxsackievirus B3 (CVB3), a compact and fast-acting RNA virus. The model consists of separable, detailed modules describing viral binding-delivery, translation-replication, and encapsidation. Specific module activities are dampened by the type I interferon response to viral double-stranded RNAs (dsRNAs), which is itself disrupted by viral proteinases. The experimentally validated kinetics uncovered that cleavability of the dsRNA transducer mitochondrial antiviral signaling protein (MAVS) becomes a stronger determinant of viral outcomes when cells receive supplemental interferon after infection. Cleavability is naturally altered in humans by a common MAVS polymorphism, which removes a proteinase-targeted site but paradoxically elevates CVB3 infectivity. These observations are reconciled with a simple nonlinear model of MAVS regulation. Modeling complete kinetics is an attainable goal for small, rapidly infecting viruses and perhaps viral pathogens more broadly. A record of this paper's transparent peer review process is included in the Supplemental information.

HIV influences clustering and intracellular replication of hepatitis C virus

HCV and HIV coinfection is common and HIV leads to increased HCV viraemia and accelerated disease progression. However, the biological basis of this interaction remains poorly understood and little is known about the impact of HIV on HCV replication at the cellular level. We analysed HCV RNA, based on single-cell laser-capture microdissection, in liver biopsies from monoinfected (n = 4) and HCV/HIV-coinfected (n = 5) participants. HCV RNA was assayed in 3200 hepatocytes with information of spatial position. We compared HCV RNA levels and clustering properties of infection between mono- and coinfected participants, and developed a mathematical model of infection. Although the median plasma HCV RNA level and the fraction of infected cells were comparable in monoinfected (7.0 log₁₀ IU/mL and ~ 30%) and coinfected (7.3 log₁₀ IU/mL and ~ 40%) participants, the median HCV RNA per infected hepatocyte in monoinfected (2.8IU) was significantly lower than in coinfected (8.2IU) participants (p = .03). Clustering of infected cells was more prominent in monoinfected participants (91% of samples) than in coinfected participants (~48%), p = .0045, suggesting that spatial spread may be influenced by HIV coinfection. Interestingly, when clustering does occur, the size of clusters is similar in both types of infection. A mathematical model of infection suggested that HIV allows higher intracellular accumulation of HCV RNA by impeding the export of HCV RNA. Our observations show that HIV coinfection impacts intracellular accumulation of HCV RNA and the clustering of HCV-infected cells, but to a less extent the fraction of HCV-infected cells.

Mathematical modeling of hepatitis C RNA replication, exosome secretion and virus release

Hepatitis C virus (HCV) causes acute hepatitis C and can lead to life-threatening complications if it becomes chronic. The HCV genome is a single plus strand of RNA. Its intracellular replication is a spatiotemporally coordinated process of RNA translation upon cell infection, RNA synthesis within a replication compartment, and virus particle production. While HCV is mainly transmitted via mature infectious virus particles, it has also been suggested that HCV-infected cells can secrete HCV RNA carrying exosomes that can infect cells in a receptor independent manner. In order to gain insight into these two routes of transmission, we developed a series of intracellular HCV replication models that include HCV RNA secretion and/or virus assembly and release. Fitting our models to in vitro data, in which cells were infected with HCV, suggests that initially most secreted HCV RNA derives from intracellular cytosolic plus-strand RNA, but subsequently secreted HCV RNA derives equally from the cytoplasm and the replication compartments. Furthermore, our model fits to the data suggest that the rate of virus assembly and release is limited by host cell resources. Including the effects of direct acting antivirals in our models, we found that in spite of decreasing intracellular HCV RNA and extracellular virus concentration, low level HCV RNA secretion may continue as long as intracellular RNA is available. This may possibly explain the presence of detectable levels of plasma HCV RNA at the end of treatment even in patients that ultimately attain a sustained virologic response.

Approximately 70 million people are chronically infected with hepatitis C virus (HCV), which if left untreated may lead to cirrhosis and liver cancer. However, modern drug therapy is highly effective and hepatitis C is the first chronic virus infection that can be cured with short-term therapy in almost all infected individuals. The within-host transmission of HCV occurs mainly via infectious virus particles, but experimental studies suggest that there may be additional receptor-independent cell-to-cell transmission by exosomes that carry the HCV genome. In order to understand the intracellular HCV lifecycle and HCV RNA spread, we developed a series of mathematical models that take both exosomal secretion and viral secretion into account. By fitting these models to in vitro data, we found that secretion of both HCV RNA as well as virus probably occurs and that the rate of virus assembly is likely limited by cellular co-factors on which the virus strongly depends for its own replication. Furthermore, our modeling predicted that the parameters governing the processes in the viral lifecycle that are targeted by direct acting antivirals are the most sensitive to perturbations, which may help explain their ability to cure this infection.

Should a viral genome stay in the host cell or leave? A quantitative dynamics study of how hepatitis C virus deals with this dilemma

Virus proliferation involves gene replication inside infected cells and transmission to new target cells. Once positive-strand RNA virus has infected a cell, the viral genome serves as a template for copying (“stay-strategy”) or is packaged into a progeny virion that will be released extracellularly (“leave-strategy”). The balance between genome replication and virion release determines virus production and transmission efficacy. The ensuing trade-off has not yet been well characterized. In this study, we use hepatitis C virus (HCV) as a model system to study the balance of the two strategies. Combining viral infection cell culture assays with mathematical modeling, we characterize the dynamics of two different HCV strains (JFH-1, a clinical isolate, and Jc1-n, a laboratory strain), which have different viral release characteristics. We found that 0.63% and 1.70% of JFH-1 and Jc1-n intracellular viral RNAs, respectively, are used for producing and releasing progeny virions. Analysis of the Malthusian parameter of the HCV genome (i.e., initial proliferation rate) and the number of de novo infections (i.e., initial transmissibility) suggests that the leave-strategy provides a higher level of initial transmission for Jc1-n, whereas, in contrast, the stay-strategy provides a higher initial proliferation rate for JFH-1. Thus, theoretical-experimental analysis of viral dynamics enables us to better understand the proliferation strategies of viruses, which contributes to the efficient control of virus transmission. Ours is the first study to analyze the stay-leave trade-off during the viral life cycle and the significance of the replication-release switching mechanism for viral proliferation.

A theoretical-experimental analysis of viral dynamics reveals a stay/leave trade-off during the viral life cycle and demonstrates the significance for viral proliferation of a replication-release switching mechanism.

Persistent Innate Immune Stimulation Results in IRF3-Mediated but Caspase-Independent Cytostasis

Persistent virus infection continuously produces non-self nucleic acids that activate cell-intrinsic immune responses. However, the antiviral defense evolved as a transient, acute phase response and the effects of persistently ongoing stimulation onto cellular homeostasis are not well understood. To study the consequences of long-term innate immune activation, we expressed the NS5B polymerase of Hepatitis C virus (HCV), which in absence of viral genomes continuously produces immune-stimulatory RNAs. Surprisingly, within 3 weeks, NS5B expression declined and the innate immune response ceased. Proteomics and functional analyses indicated a reduced proliferation of those cells most strongly stimulated, which was independent of interferon signaling but required mitochondrial antiviral signaling protein (MAVS) and interferon regulatory factor 3 (IRF3). Depletion of MAVS or IRF3, or overexpression of the MAVS-inactivating HCV NS3/4A protease not only blocked interferon responses but also restored cell growth in NS5B expressing cells. However, pan-caspase inhibition could not rescue the NS5B-induced cytostasis. Our results underline an active counter selection of cells with prolonged innate immune activation, which likely constitutes a cellular strategy to prevent persistent virus infections.

Mechanistic modeling explains the dsRNA length-dependent activation of the RIG-I mediated immune response

In cell-intrinsic antiviral immunity, cytoplasmic receptors such as retinoic acid-inducible gene I (RIG-I) detect viral double-stranded RNA (dsRNA) and trigger a signaling cascade activating the interferon (IFN) system. This leads to the transcription of hundreds of interferon-stimulated genes (ISGs) with a wide range of antiviral effects. This recognition of dsRNA not only has to be very specific to discriminate foreign from self but also highly sensitive to detect even very low numbers of pathogenic dsRNA molecules. Previous work indicated an influence of the dsRNA length on the binding behavior of RIG-I and its potential to elicit antiviral signaling. However, the molecular mechanisms behind the binding process are still under debate. We compare two hypothesized RIG-I binding mechanisms by translating them into mathematical models and analyzing their potential to describe published experimental data. The models consider the length of the dsRNA as well as known RIG-I binding motifs and describe RIG-I pathway activation after stimulation with dsRNA. We show that internal RIG-I binding sites in addition to cooperative RIG-I oligomerization are essential to describe the experimentally observed RIG-I binding behavior and immune response activation for different dsRNA lengths and concentrations. The combination of RIG-I binding to internal sites on the dsRNA and cooperative oligomerization compensates for a lack of high-affinity binding motifs and triggers a strong antiviral response for long dsRNAs. Model analysis reveals dsRNA length-dependency as a potential mechanism to discriminate between different types of dsRNAs: It allows for sensitive detection of small numbers of long dsRNAs, a typical by-product of viral replication, while ensuring tolerance against non-harming small dsRNAs.

A Coupled Mathematical Model of the Intracellular Replication of Dengue Virus and the Host Cell Immune Response to Infection

Dengue virus (DV) is a positive-strand RNA virus of the Flavivirus genus. It is one of the most prevalent mosquito-borne viruses, infecting globally 390 million individuals per year. The clinical spectrum of DV infection ranges from an asymptomatic course to severe complications such as dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS), the latter because of severe plasma leakage. Given that the outcome of infection is likely determined by the kinetics of viral replication and the antiviral host cell immune response (HIR) it is of importance to understand the interaction between these two parameters. In this study, we use mathematical modeling to characterize and understand the complex interplay between intracellular DV replication and the host cells' defense mechanisms. We first measured viral RNA, viral protein, and virus particle production in Huh7 cells, which exhibit a notoriously weak intrinsic antiviral response. Based on these measurements, we developed a detailed intracellular DV replication model. We then measured replication in IFN competent A549 cells and used this data to couple the replication model with a model describing IFN activation and production of IFN stimulated genes (ISGs), as well as their interplay with DV replication. By comparing the cell line specific DV replication, we found that host factors involved in replication complex formation and virus particle production are crucial for replication efficiency. Regarding possible modes of action of the HIR, our model fits suggest that the HIR mainly affects DV RNA translation initiation, cytosolic DV RNA degradation, and naïve cell infection. We further analyzed the potential of direct acting antiviral drugs targeting different processes of the DV lifecycle in silico and found that targeting RNA synthesis and virus assembly and release are the most promising anti-DV drug targets.

Gene Expression Profiling of Different Huh7 Variants Reveals Novel Hepatitis C Virus Host Factors

Chronic Hepatitis C virus (HCV) infection still constitutes a major global health problem with almost half a million deaths per year. To date, the human hepatoma cell line Huh7 and its derivatives is the only cell line that robustly replicates HCV. However, even different subclones and passages of this single cell line exhibit tremendous differences in HCV replication efficiency. By comparative gene expression profiling using a multi-pronged correlation analysis across eight different Huh7 variants, we identified 34 candidate host factors possibly affecting HCV permissiveness. For seven of the candidates, we could show by knock-down studies their implication in HCV replication. Notably, for at least four of them, we furthermore found that overexpression boosted HCV replication in lowly permissive Huh7 cells, most prominently for the histone-binding transcriptional repressor THAP7 and the nuclear receptor NR0B2. For NR0B2, our results suggest a finely balanced expression optimum reached in highly permissive Huh7 cells, with even higher levels leading to a nearly complete breakdown of HCV replication, likely due to a dysregulation of bile acid and cholesterol metabolism. Our unbiased expression-profiling approach, hence, led to the identification of four host cellular genes that contribute to HCV permissiveness in Huh7 cells. These findings add to an improved understanding of the molecular underpinnings of the strict host cell tropism of HCV.

Applications of Quantitative Systems Pharmacology in Model-Informed Drug Discovery: Perspective on Impact and Opportunities

Quantitative systems pharmacology (QSP) approaches have been increasingly applied in the pharmaceutical since the landmark white paper published in 2011 by a National Institutes of Health working group brought attention to the discipline. In this perspective, we discuss QSP in the context of other modeling approaches and highlight the impact of QSP across various stages of drug development and therapeutic areas. We discuss challenges to the field as well as future opportunities.

FAMoS: A Flexible and dynamic Algorithm for Model Selection to analyse complex systems dynamics

Most biological systems are difficult to analyse due to a multitude of interacting components and the concomitant lack of information about the essential dynamics. Finding appropriate models that provide a systematic description of such biological systems and that help to identify their relevant factors and processes can be challenging given the sheer number of possibilities. Model selection algorithms that evaluate the performance of a multitude of different models against experimental data provide a useful tool to identify appropriate model structures. However, many algorithms addressing the analysis of complex dynamical systems, as they are often used in biology, compare a preselected number of models or rely on exhaustive searches of the total model space which might be unfeasible dependent on the number of possibilities. Therefore, we developed an algorithm that is able to perform model selection on complex systems and searches large model spaces in a dynamical way. Our algorithm includes local and newly developed non-local search methods that can prevent the algorithm from ending up in local minima of the model space by accounting for structurally similar processes. We tested and validated the algorithm based on simulated data and showed its flexibility for handling different model structures. We also used the algorithm to analyse experimental data on the cell proliferation dynamics of CD4⁺ and CD8⁺ T cells that were cultured under different conditions. Our analyses indicated dynamical changes within the proliferation potential of cells that was reduced within tissue-like 3D ex vivo cultures compared to suspension. Due to the flexibility in handling various model structures, the algorithm is applicable to a large variety of different biological problems and represents a useful tool for the data-oriented evaluation of complex model spaces.

Identifying the systematic interactions of multiple components within a complex biological system can be challenging due to the number of potential processes and the concomitant lack of information about the essential dynamics. Selection algorithms that allow an automated evaluation of a large number of different models provide a useful tool in identifying the systematic relationships between experimental data. However, many of the existing model selection algorithms are not able to address complex model structures, such as systems of differential equations, and partly rely on local or exhaustive search methods which are inappropriate for the analysis of various biological systems. Therefore, we developed a flexible model selection algorithm that performs a robust and dynamical search of large model spaces to identify complex systems dynamics and applied it to the analysis of T cell proliferation dynamics within different culture conditions. The algorithm, which is available as an R-package, provides an advanced tool for the analysis of complex systems behaviour and, due to its flexible structure, can be applied to a large variety of biological problems.

Antiviral interferon response at single-cell resolution

Experimental studies of the innate immune response of mammalian cells to viruses reveal pervasive heterogeneity at the level of single cells. Interferons are induced only in a fraction of virus-infected cells; subsequently a fraction of cells exposed to interferons upregulate interferon-stimulated genes. Nevertheless, quantitative experiments and linked mathematical models show that the interferon response can be effective in curbing viral spread through two distinct mechanisms. First, paracrine interferon signals from scattered source cells can protect many uninfected cells, and the self-amplification of interferon production might serve to calibrate response amplitude to strength of viral infection. Second, models of the tug-of-war between viral replication and the innate interferon response imply a pivotal role of interferon action on already infected cells in curbing viral spread, through effectively lowering virus replication rate. This finding is in line with the observation that several pathogenic viruses selectively abrogate interferon action on infected cells. Thus, interferons may delay viral spread in acute infections by acting as sentinels, warning uninfected cells of imminent danger, or as negative feedback regulators of virus replication in infected cells. The timing of the interferon response relative to the onset of viral replication is critical for its effectiveness in curbing viral spread.

Model-based analysis of influenza A virus replication in genetically engineered cell lines elucidates the impact of host cell factors on key kinetic parameters of virus growth

The best measure to limit spread of contagious diseases caused by influenza A viruses (IAVs) is annual vaccination. The growing global demand for low-cost vaccines requires the establishment of high-yield production processes. One possible option to address this challenge is the engineering of novel vaccine producer cell lines by manipulating gene expression of host cell factors relevant for virus replication. To support detailed characterization of engineered cell lines, we fitted an ordinary differential equation (ODE)-based model of intracellular IAV replication previously established by our group to experimental data obtained from infection studies in human A549 cells. Model predictions indicate that steps of viral RNA synthesis, their regulation and particle assembly and virus budding are promising targets for cell line engineering. The importance of these steps was confirmed in four of five single gene overexpression cell lines (SGOs) that showed small, but reproducible changes in early dynamics of RNA synthesis and virus release. Model-based analysis suggests, however, that overexpression of the selected host cell factors negatively influences specific RNA synthesis rates. Still, virus yield was rescued by an increase in the virus release rate. Based on parameter estimations obtained for SGOs, we predicted that there is a potential benefit associated with overexpressing multiple host cell genes in one cell line, which was validated experimentally. Overall, this model-based study on IAV replication in engineered cell lines provides a step forward in the dynamic and quantitative characterization of IAV-host cell interactions. Furthermore, it suggests targets for gene editing and indicates that overexpression of multiple host cell factors may be beneficial for the design of novel producer cell lines.

Mutational pathway maps and founder effects define the within-host spectrum of hepatitis C virus mutants resistant to drugs

Knowledge of the within-host frequencies of resistance-associated amino acid variants (RAVs) is important to the identification of optimal drug combinations for the treatment of hepatitis C virus (HCV) infection. Multiple RAVs may exist in infected individuals, often below detection limits, at any resistance locus, defining the diversity of accessible resistance pathways. We developed a multiscale mathematical model to estimate the pre-treatment frequencies of the entire spectrum of mutants at chosen loci. Using a codon-level description of amino acids, we performed stochastic simulations of intracellular dynamics with every possible nucleotide variant as the infecting strain and estimated the relative infectivity of each variant and the resulting distribution of variants produced. We employed these quantities in a deterministic multi-strain model of extracellular dynamics and estimated mutant frequencies. Our predictions captured database frequencies of the RAV R155K, resistant to NS3/4A protease inhibitors, presenting a successful test of our formalism. We found that mutational pathway maps, interconnecting all viable mutants, and strong founder effects determined the mutant spectrum. The spectra were vastly different for HCV genotypes 1a and 1b, underlying their differential responses to drugs. Using a fitness landscape determined recently, we estimated that 13 amino acid variants, encoded by 44 codons, exist at the residue 93 of the NS5A protein, illustrating the massive diversity of accessible resistance pathways at specific loci. Accounting for this diversity, which our model enables, would help optimize drug combinations. Our model may be applied to describe the within-host evolution of other flaviviruses and inform vaccine design strategies.

Advanced Hepatitis C Virus Replication PDE Models within a Realistic Intracellular Geometric Environment

The hepatitis C virus (HCV) RNA replication cycle is a dynamic intracellular process occurring in three-dimensional space (3D), which is difficult both to capture experimentally and to visualize conceptually. HCV-generated replication factories are housed within virus-induced intracellular structures termed membranous webs (MW), which are derived from the Endoplasmatic Reticulum (ER). Recently, we published 3D spatiotemporal resolved diffusion⁻reaction models of the HCV RNA replication cycle by means of surface partial differential equation (sPDE) descriptions. We distinguished between the basic components of the HCV RNA replication cycle, namely HCV RNA, non-structural viral proteins (NSPs), and a host factor. In particular, we evaluated the sPDE models upon realistic reconstructed intracellular compartments (ER/MW). In this paper, we propose a significant extension of the model based upon two additional parameters: different aggregate states of HCV RNA and NSPs, and population dynamics inspired diffusion and reaction coefficients instead of multilinear ones. The combination of both aspects enables realistic modeling of viral replication at all scales. Specifically, we describe a replication complex state consisting of HCV RNA together with a defined amount of NSPs. As a result of the combination of spatial resolution and different aggregate states, the new model mimics a cis requirement for HCV RNA replication. We used heuristic parameters for our simulations, which were run only on a subsection of the ER. Nevertheless, this was sufficient to allow the fitting of core aspects of virus reproduction, at least qualitatively. Our findings should help stimulate new model approaches and experimental directions for virology.

Hepatitis C virus cell culture models: an encomium on basic research paving the road to therapy development

Chronic hepatitis C virus (HCV) infections affect 71 million people worldwide, often resulting in severe liver damage. Since 2014 highly efficient therapies based on directly acting antivirals (DAAs) are available, offering cure rates of almost 100%, if the infection is diagnosed in time. It took more than a decade to discover HCV in 1989 and another decade to establish a cell culture model. This review provides a personal view on the importance of HCV cell culture models, particularly the replicon system, in the process of therapy development, from drug screening to understanding of mode of action and resistance, with a special emphasis on the contributions of Ralf Bartenschlager's group. It summarizes the tremendous efforts of scientists in academia and industry required to achieve efficient DAAs, focusing on the main targets, protease, polymerase and NS5A. It furthermore underpins the importance of strong basic research laying the ground for translational medicine.

Mathematical Analysis of Viral Replication Dynamics and Antiviral Treatment Strategies: From Basic Models to Age-Based Multi-Scale Modeling

Viral infectious diseases are a global health concern, as is evident by recent outbreaks of the middle east respiratory syndrome, Ebola virus disease, and re-emerging zika, dengue, and chikungunya fevers. Viral epidemics are a socio-economic burden that causes short- and long-term costs for disease diagnosis and treatment as well as a loss in productivity by absenteeism. These outbreaks and their socio-economic costs underline the necessity for a precise analysis of virus-host interactions, which would help to understand disease mechanisms and to develop therapeutic interventions. The combination of quantitative measurements and dynamic mathematical modeling has increased our understanding of the within-host infection dynamics and has led to important insights into viral pathogenesis, transmission, and disease progression. Furthermore, virus-host models helped to identify drug targets, to predict the treatment duration to achieve cure, and to reduce treatment costs. In this article, we review important achievements made by mathematical modeling of viral kinetics on the extracellular, intracellular, and multi-scale level for Human Immunodeficiency Virus, Hepatitis C Virus, Influenza A Virus, Ebola Virus, Dengue Virus, and Zika Virus. Herein, we focus on basic mathematical models on the population scale (so-called target cell-limited models), detailed models regarding the most important steps in the viral life cycle, and the combination of both. For this purpose, we review how mathematical modeling of viral dynamics helped to understand the virus-host interactions and disease progression or clearance. Additionally, we review different types and effects of therapeutic strategies and how mathematical modeling has been used to predict new treatment regimens.

Modelling how responsiveness to interferon improves interferon-free treatment of hepatitis C virus infection

Direct-acting antiviral agents (DAAs) for hepatitis C treatment tend to fare better in individuals who are also likely to respond well to interferon-alpha (IFN), a surprising correlation given that DAAs target specific viral proteins whereas IFN triggers a generic antiviral immune response. Here, we posit a causal relationship between IFN-responsiveness and DAA treatment outcome. IFN-responsiveness restricts viral replication, which would prevent the growth of viral variants resistant to DAAs and improve treatment outcome. To test this hypothesis, we developed a multiscale mathematical model integrating IFN-responsiveness at the cellular level, viral kinetics and evolution leading to drug resistance at the individual level, and treatment outcome at the population level. Model predictions quantitatively captured data from over 50 clinical trials demonstrating poorer response to DAAs in previous non-responders to IFN than treatment-naïve individuals, presenting strong evidence supporting the hypothesis. Model predictions additionally described several unexplained clinical observations, viz., the percentages of infected individuals who 1) spontaneously clear HCV, 2) get chronically infected but respond to IFN-based therapy, and 3) fail IFN-based therapy but respond to DAA-based therapy, resulting in a comprehensive understanding of HCV infection and treatment. An implication of the causal relationship is that failure of DAA-based treatments may be averted by adding IFN, a strategy of potential use in settings with limited access to DAAs. A second, wider implication is that individuals with greater IFN-responsiveness would require shorter DAA-based treatment durations, presenting a basis and a promising population for response-guided therapy.

Intracellular Hepatitis C Virus Modeling Predicts Infection Dynamics and Viral Protein Mechanisms

Hepatitis C virus (HCV) infection is a global health problem, with nearly 2 million new infections occurring every year and up to 85% of these infections becoming chronic infections that pose serious long-term health risks. To effectively reduce the prevalence of HCV infection and associated diseases, it is important to understand the intracellular dynamics of the viral life cycle. Here, we present a detailed mathematical model that represents the full hepatitis C virus life cycle. It is the first full HCV model to be fit to acute intracellular infection data and the first to explore the functions of distinct viral proteins, probing multiple hypotheses of cis- and trans-acting mechanisms to provide insights for drug targeting. Model parameters were derived from the literature, experiments, and fitting to experimental intracellular viral RNA, extracellular viral titer, and HCV core and NS3 protein kinetic data from viral inoculation to steady state. Our model predicts higher rates for protein translation and polyprotein cleavage than previous replicon models and demonstrates that the processes of translation and synthesis of viral RNA have the most influence on the levels of the species we tracked in experiments. Overall, our experimental data and the resulting mathematical infection model reveal information about the regulation of core protein during infection, produce specific insights into the roles of the viral core, NS5A, and NS5B proteins, and demonstrate the sensitivities of viral proteins and RNA to distinct reactions within the life cycle.IMPORTANCE We have designed a model for the full life cycle of hepatitis C virus. Past efforts have largely focused on modeling hepatitis C virus replicon systems, in which transfected subgenomic HCV RNA maintains autonomous replication in the absence of virion production or spread. We started with the general structure of these previous replicon models and expanded it to create a model that incorporates the full virus life cycle as well as additional intracellular mechanistic detail. We compared several different hypotheses that have been proposed for different parts of the life cycle and applied the corresponding model variations to infection data to determine which hypotheses are most consistent with the empirical kinetic data. Because the infection data we have collected for this study are a more physiologically relevant representation of a viral life cycle than data obtained from a replicon system, our model can make more accurate predictions about clinical hepatitis C virus infections.

A New Age-Structured Multiscale Model of the Hepatitis C Virus Life-Cycle During Infection and Therapy With Direct-Acting Antiviral Agents

The dynamics of hepatitis C virus (HCV) RNA during translation and replication within infected cells were added to a previous age-structured multiscale mathematical model of HCV infection and treatment. The model allows the study of the dynamics of HCV RNA inside infected cells as well as the release of virus from infected cells and the dynamics of subsequent new cell infections. The model was used to fit in vitro data and estimate parameters characterizing HCV replication. This is the first model to our knowledge to consider both positive and negative strands of HCV RNA with an age-structured multiscale modeling approach. Using this model we also studied the effects of direct-acting antiviral agents (DAAs) in blocking HCV RNA intracellular replication and the release of new virions and fit the model to in vivo data obtained from HCV-infected subjects under therapy.

Quantitative Analysis of Hepatitis C NS5A Viral Protein Dynamics on the ER Surface

Exploring biophysical properties of virus-encoded components and their requirement for virus replication is an exciting new area of interdisciplinary virological research. To date, spatial resolution has only rarely been analyzed in computational/biophysical descriptions of virus replication dynamics. However, it is widely acknowledged that intracellular spatial dependence is a crucial component of virus life cycles. The hepatitis C virus-encoded NS5A protein is an endoplasmatic reticulum (ER)-anchored viral protein and an essential component of the virus replication machinery. Therefore, we simulate NS5A dynamics on realistic reconstructed, curved ER surfaces by means of surface partial differential equations (sPDE) upon unstructured grids. We match the in silico NS5A diffusion constant such that the NS5A sPDE simulation data reproduce experimental NS5A fluorescence recovery after photobleaching (FRAP) time series data. This parameter estimation yields the NS5A diffusion constant. Such parameters are needed for spatial models of HCV dynamics, which we are developing in parallel but remain qualitative at this stage. Thus, our present study likely provides the first quantitative biophysical description of the movement of a viral component. Our spatio-temporal resolved ansatz paves new ways for understanding intricate spatial-defined processes central to specfic aspects of virus life cycles.

3D Spatially Resolved Models of the Intracellular Dynamics of the Hepatitis C Genome Replication Cycle

Mathematical models of virus dynamics have not previously acknowledged spatial resolution at the intracellular level despite substantial arguments that favor the consideration of intracellular spatial dependence. The replication of the hepatitis C virus (HCV) viral RNA (vRNA) occurs within special replication complexes formed from membranes derived from endoplasmatic reticulum (ER). These regions, termed membranous webs, are generated primarily through specific interactions between nonstructural virus-encoded proteins (NSPs) and host cellular factors. The NSPs are responsible for the replication of the vRNA and their movement is restricted to the ER surface. Therefore, in this study we developed fully spatio-temporal resolved models of the vRNA replication cycle of HCV. Our simulations are performed upon realistic reconstructed cell structures-namely the ER surface and the membranous webs-based on data derived from immunostained cells replicating HCV vRNA. We visualized 3D simulations that reproduced dynamics resulting from interplay of the different components of our models (vRNA, NSPs, and a host factor), and we present an evaluation of the concentrations for the components within different regions of the cell. Thus far, our model is restricted to an internal portion of a hepatocyte and is qualitative more than quantitative. For a quantitative adaption to complete cells, various additional parameters will have to be determined through further in vitro cell biology experiments, which can be stimulated by the results deccribed in the present study.

Aggregation of a hepatitis C virus replicase module induced by ablation of p97/VCP

Hijacking host membranes to assemble a membrane-associated viral replicase is a hallmark of almost all positive-strand RNA viruses. However, how the virus co-opts host factors to facilitate this energy-unfavourable process is incompletely understood. In a previous study, using hepatitis C virus (HCV) as a model and employing affinity purification of the viral replicase, we identified a valosin-containing protein (p97/VCP), a member of the ATPases associated with diverse cellular activities (AAA+ ATPase family), as a viral replicase-associated host factor. It is required for viral replication, depending on its ATPase activity. In this study, we used VCP pharmacological inhibitors and short hairpin (sh) RNA-mediated knockdown to ablate VCP function and then dissected the roles of VCP in viral replicase assembly in an HCV subgenomic replicon system and a viral replicase assembly surrogate system. Ablation of VCP specifically resulted in the pronounced formation of an SDS-resistant aggregation of HCV NS5A and the reduction of hyperphosphorylation of NS5A. The NS5A dimerization domain was indispensable for aggregation and the NS5A disordered regions also contributed to a lesser extent. The reduction of the hyperphosphorylation of NS5A coincided with the aggregation of NS5A. We propose that HCV may co-opt VCP to disaggregate an aggregation-prone replicase module to facilitate its replicase assembly.

NS5A inhibitors unmask differences in functional replicase complex half-life between different hepatitis C virus strains

Hepatitis C virus (HCV) RNA is synthesized by the replicase complex (RC), a macromolecular assembly composed of viral non-structural proteins and cellular co-factors. Inhibitors of the HCV NS5A protein block formation of new RCs but do not affect RNA synthesis by pre-formed RCs. Without new RC formation, existing RCs turn over and are eventually lost from the cell. We aimed to use NS5A inhibitors to estimate the half-life of the functional RC of HCV. We compared different cell culture-infectious strains of HCV that may be grouped based on their sensitivity to lipid peroxidation: robustly replicating, lipid peroxidation resistant (LPOR) viruses (e.g. JFH-1 or H77D) and more slowly replicating, lipid peroxidation sensitive (LPOS) viruses (e.g. H77S.3 and N.2). In luciferase assays, LPOS HCV strains declined under NS5A inhibitor therapy with much slower kinetics compared to LPOR HCV strains. This difference in rate of decline was not observed for inhibitors of the NS5B RNA-dependent RNA polymerase suggesting that the difference was not simply a consequence of differences in RNA stability. In further analyses, we compared two isoclonal HCV variants: the LPOS H77S.3 and the LPOR H77D that differ only by 12 amino acids. Differences in rate of decline between H77S.3 and H77D following NS5A inhibitor addition were not due to amino acid sequences in NS5A but rather due to a combination of amino acid differences in the non-structural proteins that make up the HCV RC. Mathematical modeling of intracellular HCV RNA dynamics suggested that differences in RC stability (half-lives of 3.5 and 9.9 hours, for H77D and H77S.3, respectively) are responsible for the different kinetics of antiviral suppression between LPOS and LPOR viruses. In nascent RNA capture assays, the rate of RNA synthesis decline following NS5A inhibitor addition was significantly faster for H77D compared to H77S.3 indicating different half-lives of functional RCs.

Oxidative stress, a trigger of hepatitis C and B virus-induced liver carcinogenesis

Virally induced liver cancer usually evolves over long periods of time in the context of a strongly oxidative microenvironment, characterized by chronic liver inflammation and regeneration processes. They ultimately lead to oncogenic mutations in many cellular signaling cascades that drive cell growth and proliferation. Oxidative stress, induced by hepatitis viruses, therefore is one of the factors that drives the neoplastic transformation process in the liver. This review summarizes current knowledge on oxidative stress and oxidative stress responses induced by human hepatitis B and C viruses. It focuses on the molecular mechanisms by which these viruses activate cellular enzymes/systems that generate or scavenge reactive oxygen species (ROS) and control cellular redox homeostasis. The impact of an altered cellular redox homeostasis on the initiation and establishment of chronic viral infection, as well as on the course and outcome of liver fibrosis and hepatocarcinogenesis will be discussed The review neither discusses reactive nitrogen species, although their metabolism is interferes with that of ROS, nor antioxidants as potential therapeutic remedies against viral infections, both subjects meriting an independent review.

The HCV Replicase Complex and Viral RNA Synthesis

Replication of hepatitis C virus (HCV) is tightly linked to membrane alterations designated the membranous web, harboring the viral replicase complex. In this chapter we describe the morphology and 3D architecture of the HCV-induced replication organelles, mainly consisting of double membrane vesicles, which are generated by a concerted action of the nonstructural proteins NS3 to NS5B. Recent studies have furthermore identified a number of host cell proteins and lipids contributing to the biogenesis of the membranous web, which are discussed in this chapter. Viral RNA synthesis is tightly associated with these membrane alterations and mainly driven by the viral RNA dependent RNA polymerase NS5B. We summarize our current knowledge of the structure and function of NS5B, the role of cis-acting replication elements at the termini of the genome in regulating RNA synthesis and the contribution of additional viral and host factors to viral RNA synthesis, which is still ill defined.

Viral RNA Degradation and Diffusion Act as a Bottleneck for the Influenza A Virus Infection Efficiency

After endocytic uptake, influenza viruses transit early endosomal compartments and eventually reach late endosomes. There, the viral glycoprotein hemagglutinin (HA) triggers fusion between endosomal and viral membrane, a critical step that leads to release of the viral segmented genome destined to reach the cell nucleus. Endosomal maturation is a complex process involving acidification of the endosomal lumen as well as endosome motility along microtubules. While the pH drop is clearly critical for the conformational change and membrane fusion activity of HA, the effect of intracellular transport dynamics on the progress of infection remains largely unclear. In this study, we developed a comprehensive mathematical model accounting for the first steps of influenza virus infection. We calibrated our model with experimental data and challenged its predictions using recombinant viruses with altered pH sensitivity of HA. We identified the time point of virus-endosome fusion and thereby the diffusion distance of the released viral genome to the nucleus as a critical bottleneck for efficient virus infection. Further, we concluded and supported experimentally that the viral RNA is subjected to cytosolic degradation strongly limiting the probability of a successful genome import into the nucleus.

Influenza A virus carries its segmented genome inside a lipid envelope. Since genome replication occurs inside the nucleus, the main goal of virus infection is to deliver all genome segments through the cytoplasm into the nucleus. After endocytic uptake, influenza viruses transit early endosomal compartments and eventually reach late endosomes. Within a complex maturation process, the endosomal lumen acidifies while the vesicles are transported trough the cytosol. If and how these early processes affect virus infection remained mostly speculative. To reach a better understanding and to quantify the physical interplay between membrane fusion, genome diffusion and infection, we developed a mathematical model that comprises all initial steps of virus infection until genome delivery. We calibrated our model using experimental data and challenged its predictions using recombinant viruses to introduce perturbations. Our results provide a theoretical framework to understand the importance of the endosomal virus passage before membrane fusion and genome release. We further unraveled RNA degradation as a previously unknown limiting factor for virus infection. Our work will help to make predictions and evaluate newly occurring virus strains, regarding their infection efficiency in a given host cell, by simply considering their pH sensitivity.

ZIKA virus reveals broad tissue and cell tropism during the first trimester of pregnancy

The outbreak of the Zika Virus (ZIKV) and its association with fetal abnormalities have raised worldwide concern. However, the cellular tropism and the mechanisms of ZIKV transmission to the fetus during early pregnancy are still largely unknown. Therefore, we ex vivo modeled the ZIKV transmission at the maternal-fetal interface using organ culture from first trimester pregnancy samples. Here, we provide evidence that ZIKV strain circulating in Brazil infects and damages tissue architecture of the maternal decidua basalis, the fetal placenta and umbilical cord. We also show that ZIKV replicates differentially in a wide range of maternal and fetal cells, including decidual fibroblasts and macrophages, trophoblasts, Hofbauer cells as well as umbilical cord mesenchymal stem cells. The striking cellular tropism of ZIKV and its cytopathic-induced tissue injury during the first trimester of pregnancy could provide an explanation for the irreversible congenital damages.

Modeling Viral Spread

The way in which a viral infection spreads within a host is a complex process that is not well understood. Different viruses, such as human immunodeficiency virus type 1 and hepatitis C virus, have evolved different strategies, including direct cell-to-cell transmission and cell-free transmission, to spread within a host. To what extent these two modes of transmission are exploited in vivo is still unknown. Mathematical modeling has been an essential tool to get a better systematic and quantitative understanding of viral processes that are difficult to discern through strictly experimental approaches. In this review, we discuss recent attempts that combine experimental data and mathematical modeling in order to determine and quantify viral transmission modes. We also discuss the current challenges for a systems-level understanding of viral spread, and we highlight the promises and challenges that novel experimental techniques and data will bring to the field.

Models of Viral Population Dynamics

Models of viral population dynamics have contributed enormously to our understanding of the pathogenesis and transmission of several infectious diseases, the coevolutionary dynamics of viruses and their hosts, the mechanisms of action of drugs, and the effectiveness of interventions. In this chapter, we review major advances in the modeling of the population dynamics of the human immunodeficiency virus (HIV) and briefly discuss adaptations to other viruses.

Reprint of "Modeling the intracellular replication of influenza A virus in the presence of defective interfering RNAs

Like many other viral pathogens, influenza A viruses can form defective interfering particles (DIPs). These particles carry a large internal deletion in at least one of their genome segments. Thus, their replication depends on the co-infection of cells by standard viruses (STVs), which supply the viral protein(s) encoded by the defective segment. However, DIPs also interfere with STV replication at the molecular level and, despite considerable research efforts, the mechanism of this interference remains largely elusive. Here, we present a mechanistic mathematical model for the intracellular replication of DIPs. In this model, we account for the common hypothesis that defective interfering RNAs (DI RNAs) possess a replication advantage over full-length (FL) RNAs due to their reduced length. By this means, the model captures experimental data from yield reduction assays and from studies testing different co-infection timings. In addition, our model predicts that one important aspect of interference is the competition for viral proteins, namely the heterotrimeric viral RNA-dependent RNA polymerase (RdRp) and the viral nucleoprotein (NP), which are needed for encapsidation of naked viral RNA. Moreover, we find that there may be an optimum for both the DI RNA synthesis rate and the time point of successive co-infection of a cell by DIPs and STVs. Comparing simulations for the growth of DIPs with a deletion in different genome segments suggests that DI RNAs derived from segments which encode for the polymerase subunits are more competitive than others. Overall, our model, thus, helps to elucidate the interference mechanism of DI RNAs and provides a novel hypothesis why DI RNAs derived from the polymerase-encoding segments are more abundant in DIP preparations.

Distinct Roles for Intracellular and Extracellular Lipids in Hepatitis C Virus Infection

Hepatitis C is a chronic liver disease that contributes to progressive metabolic dysfunction. Infection of hepatocytes by hepatitis C virus (HCV) results in reprogramming of hepatic and serum lipids. However, the specific contribution of these distinct pools of lipids to HCV infection remains ill defined. In this study, we investigated the role of hepatic lipogenesis in HCV infection by targeting the rate-limiting step in this pathway, which is catalyzed by the acetyl-CoA carboxylase (ACC) enzymes. Using two structurally unrelated ACC inhibitors, we determined that blockade of lipogenesis resulted in reduced viral replication, assembly, and release. Supplementing exogenous lipids to cells treated with ACC inhibitors rescued HCV assembly with no effect on viral replication and release. Intriguingly, loss of viral RNA was not recapitulated at the protein level and addition of 2-bromopalmitate, a competitive inhibitor of protein palmitoylation, mirrored the effects of ACC inhibitors on reduced viral RNA without a concurrent loss in protein expression. These correlative results suggest that newly synthesized lipids may have a role in protein palmitoylation during HCV infection.

Viral genome imaging of hepatitis C virus to probe heterogeneous viral infection and responses to antiviral therapies

Hepatitis C virus (HCV) is a positive single-stranded RNA virus of enormous global health importance, with direct-acting antiviral therapies replacing an immunostimulatory interferon-based regimen. The dynamics of HCV positive and negative-strand viral RNAs (vRNAs) under antiviral perturbations have not been studied at the single-cell level, leaving a gap in our understanding of antiviral kinetics and host-virus interactions. Here, we demonstrate quantitative imaging of HCV genomes in multiple infection models, and multiplexing of positive and negative strand vRNAs and host antiviral RNAs. We capture the varying kinetics with which antiviral drugs with different mechanisms of action clear HCV infection, finding the NS5A inhibitor daclatasvir to induce a rapid decline in negative-strand viral RNAs. We also find that the induction of host antiviral genes upon interferon treatment is positively correlated with viral load in single cells. This study adds smFISH to the toolbox available for analyzing the treatment of RNA virus infections.

Highly heterogeneous mutation rates in the hepatitis C virus genome

Spontaneous mutations are the ultimate source of genetic variation and have a prominent role in evolution. RNA viruses such as hepatitis C virus (HCV) have extremely high mutation rates, but these rates have been inferred from a minute fraction of genome sites, limiting our view of how RNA viruses create diversity. Here, by applying high-fidelity ultradeep sequencing to a modified replicon system, we scored >15,000 spontaneous mutations, encompassing more than 90% of the HCV genome. This revealed >1,000-fold differences in mutability across genome sites, with extreme variations even between adjacent nucleotides. We identify base composition, the presence of high- and low-mutation clusters and transition/transversion biases as the main factors driving this heterogeneity. Furthermore, we find that mutability correlates with the ability of HCV to diversify in patients. These data provide a site-wise baseline for interrogating natural selection, genetic load and evolvability in HCV, as well as for evaluating drug resistance and immune evasion risks.

Multiscale modeling of virus replication and spread

Replication and spread of human viruses is based on the simultaneous exploitation of many different host functions, bridging multiple scales in space and time. Mathematical modeling is essential to obtain a systems-level understanding of how human viruses manage to proceed through their life cycles. Here, we review corresponding advances for viral systems of large medical relevance, such as human immunodeficiency virus-1 (HIV-1) and hepatitis C virus (HCV). We will outline how the combination of mathematical models and experimental data has advanced our quantitative knowledge about various processes of these pathogens, and how novel quantitative approaches promise to fill remaining gaps.

Third-Kind Encounters in Biomedicine: Immunology Meets Mathematics and Informatics to Become Quantitative and Predictive

The understanding of the immune response is right now at the center of biomedical research. There are growing expectations that immune-based interventions will in the midterm provide new, personalized, and targeted therapeutic options for many severe and highly prevalent diseases, from aggressive cancers to infectious and autoimmune diseases. To this end, immunology should surpass its current descriptive and phenomenological nature, and become quantitative, and thereby predictive.Immunology is an ideal field for deploying the tools, methodologies, and philosophy of systems biology, an approach that combines quantitative experimental data, computational biology, and mathematical modeling. This is because, from an organism-wide perspective, the immunity is a biological system of systems, a paradigmatic instance of a multi-scale system. At the molecular scale, the critical phenotypic responses of immune cells are governed by large biochemical networks, enriched in nested regulatory motifs such as feedback and feedforward loops. This network complexity confers them the ability of highly nonlinear behavior, including remarkable examples of homeostasis, ultra-sensitivity, hysteresis, and bistability. Moving from the cellular level, different immune cell populations communicate with each other by direct physical contact or receiving and secreting signaling molecules such as cytokines. Moreover, the interaction of the immune system with its potential targets (e.g., pathogens or tumor cells) is far from simple, as it involves a number of attack and counterattack mechanisms that ultimately constitute a tightly regulated multi-feedback loop system. From a more practical perspective, this leads to the consequence that today's immunologists are facing an ever-increasing challenge of integrating massive quantities from multi-platforms.In this chapter, we support the idea that the analysis of the immune system demands the use of systems-level approaches to ensure the success in the search for more effective and personalized immune-based therapies.

Live Cell Analysis and Mathematical Modeling Identify Determinants of Attenuation of Dengue Virus 2'-O-Methylation Mutant

Dengue virus (DENV) is the most common mosquito-transmitted virus infecting ~390 million people worldwide. In spite of this high medical relevance, neither a vaccine nor antiviral therapy is currently available. DENV elicits a strong interferon (IFN) response in infected cells, but at the same time actively counteracts IFN production and signaling. Although the kinetics of activation of this innate antiviral defense and the timing of viral counteraction critically determine the magnitude of infection and thus disease, quantitative and kinetic analyses are lacking and it remains poorly understood how DENV spreads in IFN-competent cell systems. To dissect the dynamics of replication versus antiviral defense at the single cell level, we generated a fully viable reporter DENV and host cells with authentic reporters for IFN-stimulated antiviral genes. We find that IFN controls DENV infection in a kinetically determined manner that at the single cell level is highly heterogeneous and stochastic. Even at high-dose, IFN does not fully protect all cells in the culture and, therefore, viral spread occurs even in the face of antiviral protection of naïve cells by IFN. By contrast, a vaccine candidate DENV mutant, which lacks 2’-O-methylation of viral RNA is profoundly attenuated in IFN-competent cells. Through mathematical modeling of time-resolved data and validation experiments we show that the primary determinant for attenuation is the accelerated kinetics of IFN production. This rapid induction triggered by mutant DENV precedes establishment of IFN-resistance in infected cells, thus causing a massive reduction of virus production rate. In contrast, accelerated protection of naïve cells by paracrine IFN action has negligible impact. In conclusion, these results show that attenuation of the 2’-O-methylation DENV mutant is primarily determined by kinetics of autocrine IFN action on infected cells.

Dengue virus (DENV) infection is a global health problem for which no selective therapy or vaccine exists. The magnitude of infection critically depends on the induction kinetics of the interferon (IFN) response and the kinetics of viral countermeasures. Here we established a novel live cell imaging system to dissect the dynamics of this interplay. We find that IFN controls DENV infection in a kinetically determined manner. At the single cell level, the IFN response is highly heterogeneous and stochastic, likely accounting for viral spread in the presence of IFN. Mathematical modeling and validation experiments show that the kinetics of activation of the IFN response critically determines control of virus replication and spread. A vaccine candidate DENV mutant lacking 2’-O-methylation of viral RNA is profoundly attenuated in IFN-competent cells. This attenuation is primarily due to accelerated kinetics of IFN production acting on infected cells in an autocrine manner. In contrast, accelerated protection of naïve cells by paracrine IFN action has negligible impact. Thus, attenuation of the 2’-O-methylation DENV mutant is primarily determined by kinetics of autocrine IFN action on infected cells.

Modeling the intracellular replication of influenza A virus in the presence of defective interfering RNAs

Like many other viral pathogens, influenza A viruses can form defective interfering particles (DIPs). These particles carry a large internal deletion in at least one of their genome segments. Thus, their replication depends on the co-infection of cells by standard viruses (STVs), which supply the viral protein(s) encoded by the defective segment. However, DIPs also interfere with STV replication at the molecular level and, despite considerable research efforts, the mechanism of this interference remains largely elusive. Here, we present a mechanistic mathematical model for the intracellular replication of DIPs. In this model, we account for the common hypothesis that defective interfering RNAs (DI RNAs) possess a replication advantage over full-length (FL) RNAs due to their reduced length. By this means, the model captures experimental data from yield reduction assays and from studies testing different co-infection timings. In addition, our model predicts that one important aspect of interference is the competition for viral proteins, namely the heterotrimeric viral RNA-dependent RNA polymerase (RdRp) and the viral nucleoprotein (NP), which are needed for encapsidation of naked viral RNA. Moreover, we find that there may be an optimum for both the DI RNA synthesis rate and the time point of successive co-infection of a cell by DIPs and STVs. Comparing simulations for the growth of DIPs with a deletion in different genome segments suggests that DI RNAs derived from segments which encode for the polymerase subunits are more competitive than others. Overall, our model, thus, helps to elucidate the interference mechanism of DI RNAs and provides a novel hypothesis why DI RNAs derived from the polymerase-encoding segments are more abundant in DIP preparations.