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Influenza Viruses and mRNA Splicing: Doing More with Less

During their nuclear replication stage, influenza viruses hijack the host splicing machinery to process some of their RNA segments, the M and NS segments. In this review, we provide an overview of the current knowledge gathered on this interplay between influenza viruses and the cellular spliceosome, with a particular focus on influenza A viruses (IAV). These viruses have developed accurate regulation mechanisms to reassign the host spliceosome to alter host cellular expression and enable an optimal expression of specific spliced viral products throughout infection. Moreover, IAV segments undergoing splicing display high levels of similarity with human consensus splice sites and their viral transcripts show noteworthy secondary structures. Sequence alignments and consensus analyses, along with recently published studies, suggest both conservation and evolution of viral splice site sequences and structure for improved adaptation to the host. Altogether, these results emphasize the ability of IAV to be well adapted to the host’s splicing machinery, and further investigations may contribute to a better understanding of splicing regulation with regard to viral replication, host range, and pathogenesis

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Nice review of RNA splicing in influenza virus.

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Distinct kinetics and pathways of apoptosis in influenza A and B virus infection

Distinct kinetics and pathways of apoptosis in influenza A and B virus infection | Viral Modeling and Simulation | Scoop.it
Highlights

 • Differences in apoptosis kinetics of Influenza A and B cell infection were observed.

• Both influenza A and B infection activate the PI3K/Akt survival pathway.• IκB/NF-κB survival pathway acts as major contributor for the differences observed.
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Estimating the dynamics and dependencies of accumulating mutations with applications to HIV drug resistance

Estimating the dynamics and dependencies of accumulating mutations with applications to HIV drug resistance | Viral Modeling and Simulation | Scoop.it

We introduce a new model called the observed time conjunctive Bayesian network (OT-CBN) that describes the accumulation of genetic events (mutations) under partial temporal ordering constraints. Unlike other CBN models, the OT-CBN model uses sampling time points of genotypes in addition to genotypes themselves to estimate model parameters. We developed an expectation–maximization algorithm to obtain approximate maximum likelihood estimates by accounting for this additional information. In a simulation study, we show that the OT-CBN model outperforms the continuous time CBN (CT-CBN) (Beerenwinkel and Sullivant, 2009. Markov models for accumulating mutations. Biometrika96(3), 645–661), which does not take into account individual sampling times for parameter estimation. We also show superiority of the OT-CBN model on several datasets of HIV drug resistance mutations extracted from the Swiss HIV Cohort Study database.

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A Hepatitis C Virus Infection Model with Time-Varying Drug Effectiveness: Solution and Analysis

A Hepatitis C Virus Infection Model with Time-Varying Drug Effectiveness: Solution and Analysis | Viral Modeling and Simulation | Scoop.it
Author Summary Fitting simple models of therapy for viral diseases, such as hepatitis C virus (HCV) or human immunodeficiency virus, to patient data has yielded significant insights into the underlying viral dynamics. In general, these models assume that, once therapy is started, the drug has a constant effectiveness. More realistic assumptions are that drug effectiveness either depends directly on the drug concentration or varies over time. Here a previously introduced varying-effectiveness
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Role of lipids in virus assembly | Frontiers Editorial: Virology

Viruses utilize cellular lipids during critical steps of replication like entry, assembly and egress. Growing evidence indicate important roles for lipids and lipid nanodomains in virus assembly. T...
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A conservation law for virus infection kinetics in vitro

A conservation law for virus infection kinetics in vitro | Viral Modeling and Simulation | Scoop.it
Conservation laws are among the most important properties of a physical system, but are not commonplace in biology. We derived a conservation law from the basic model for viral infections which consists in a small set of ordinary differential equations. We challenged the conservation law experimentally for the case of a virus infection in a cell culture. We found that the derived, conserved quantity remained almost constant throughout the infection period, implying that the derived conservation law holds in this biological system. We also suggest a potential use for the conservation law in evaluating the accuracy of experimental measurements.
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Multi-scale modeling for the transmission of influenza and the evaluation of interventions toward it : Nature

Multi-scale modeling for the transmission of influenza and the evaluation of interventions toward it : Nature | Viral Modeling and Simulation | Scoop.it
Mathematical modeling of influenza epidemic is important for analyzing the main cause of the epidemic and finding effective interventions towards it. The epidemic is a dynamic process. In this process, daily infections are caused by people's contacts, and the frequency of contacts can be mainly influenced by their cognition to the disease. The cognition is in turn influenced by daily illness attack rate, climate, and other environment factors. Few existing methods considered the dynamic process in their models. Therefore, their prediction results can hardly be explained by the mechanisms of epidemic spreading. In this paper, we developed a heterogeneous graph modeling approach (HGM) to describe the dynamic process of influenza virus transmission by taking advantage of our unique clinical data. We built social network of studied region and embedded an Agent-Based Model (ABM) in the HGM to describe the dynamic change of an epidemic. Our simulations have a good agreement with clinical data. Parameter sensitivity analysis showed that temperature influences the dynamic of epidemic significantly and system behavior analysis showed social network degree is a critical factor determining the size of an epidemic. Finally, multiple scenarios for vaccination and school closure strategies were simulated and their performance was analyzed.
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A Super-Quick Introduction to BioFabric

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This technique offers a nice replacement of the traditional biological network hairballs data visualization.

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Estimating the Life Course of Influenza A(H3N2) Antibody Responses ... - PubMed - NCBI

Abstract

The immunity of a host population against specific influenza A strains can influence a number of important biological processes, from the emergence of new virus strains to the effectiveness of vaccination programmes. However, the development of an individual's long-lived antibody response to influenza A over the course of a lifetime remains poorly understood. Accurately describing this immunological process requires a fundamental understanding of how the mechanisms of boosting and cross-reactivity respond to repeated infections. Establishing the contribution of such mechanisms to antibody titres remains challenging because the aggregate effect of immune responses over a lifetime are rarely observed directly. To uncover the aggregate effect of multiple influenza infections, we developed a mechanistic model capturing both past infections and subsequent antibody responses. We estimated parameters of the model using cross-sectional antibody titres to nine different strains spanning 40 years of circulation of influenza A(H3N2) in southern China. We found that "antigenic seniority" and quickly decaying cross-reactivity were important components of the immune response, suggesting that the order in which individuals were infected with influenza strains shaped observed neutralisation titres to a particular virus. We also obtained estimates of the frequency and age distribution of influenza infection, which indicate that although infections became less frequent as individuals progressed through childhood and young adulthood, they occurred at similar rates for individuals above age 30 y. By establishing what are likely to be important mechanisms driving epochal trends in population immunity, we also identified key directions for future studies. In particular, our results highlight the need for longitudinal samples that are tested against multiple historical strains. This could lead to a better understanding of how, over the course of a lifetime, fast, transient antibody dynamics combine with the longer-term immune responses considered here

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Interferons and viruses: an evolutionary arms race of molecular interactions: Trends in Immunology

Highlights Pathogen recognition leads to the production of many similar interferons.Interferons signal through the same receptor but can have different effects.Viruses antagonize interferon induction and signaling.Innate immune activators are effective vaccine adjuvants.

 

Over half a century has passed since interferons (IFNs) were discovered and shown to inhibit virus infection in cultured cells. Since then, researchers have steadily brought to light the molecular details of IFN signaling, catalogued their pleiotropic effects on cells, and harnessed their therapeutic potential for a variety of maladies. While advances have been plentiful, several fundamental questions have yet to be answered and much complexity remains to be unraveled. We explore the current knowledge surrounding four main questions: are type I IFN subtypes differentially produced in response to distinct pathogens? How are IFN subtypes distinguished by cells? What are the mechanisms and consequences of viral antagonism? Lastly, how can the IFN response be harnessed to improve vaccine efficacy?

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Structural characterization of a protective epitope spanning A(H1N1)pdm09 influenza virus neuraminidase monomers : Nature

Structural characterization of a protective epitope spanning A(H1N1)pdm09 influenza virus neuraminidase monomers : Nature | Viral Modeling and Simulation | Scoop.it

A(H1N1)pdm09 influenza A viruses predominated in the 2013–2014 USA influenza season, and although most of these viruses remain sensitive to Food and Drug Administration-approved neuraminidase (NA) inhibitors, alternative therapies are needed. Here we show that monoclonal antibody CD6, selected for binding to the NA of the prototypic A(H1N1)pdm09 virus, A/California/07/2009, protects mice against lethal virus challenge. The crystal structure of NA in complex with CD6 Fab reveals a unique epitope, where the heavy-chain complementarity determining regions (HCDRs) 1 and 2 bind one NA monomer, the light-chain CDR2 binds the neighbouring monomer, whereas HCDR3 interacts with both monomers. This 30-amino-acid epitope spans the lateral face of an NA dimer and is conserved among circulating A(H1N1)pdm09 viruses. These results suggest that the large, lateral CD6 epitope may be an effective target of antibodies selected for development as therapeutic agents against circulating H1N1 influenza viruses.

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Predictive Validation of an Influenza Spread Model

Predictive Validation of an Influenza Spread Model | Viral Modeling and Simulation | Scoop.it
Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the pre
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Watch how the measles outbreak spreads when kids get vaccinated – and when they don't

Watch how the measles outbreak spreads when kids get vaccinated – and when they don't | Viral Modeling and Simulation | Scoop.it
If you take 10 communities and run a simulation, it’s easy to see why we need as many members of the ‘herd’ as possible to get vaccines – before it’s too late
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geppetto

geppetto | Viral Modeling and Simulation | Scoop.it
Computational biology reimagined.

Geppetto is a web-based multi-algorithm, multi-scale simulation platform engineered to support the simulation of complex biological systems and their surrounding environment.

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Frontiers | A review on computational systems biology of pathogen–host interactions | Infectious Diseases

Pathogens manipulate the cellular mechanisms of host organisms via Pathogen-Host Interactions (PHIs) in order to take advantage of the capabilities of host cells, leading to infections. The crucial role of these interspecies molecular interactions in initiating and sustaining infections necessitates a thorough understanding of the corresponding mechanisms. Unlike the traditional approach of considering the host or pathogen separately, a systems-level approach, considering the PHI system as a whole is indispensable to elucidate the mechanisms of infection. Following the technological advances in the post-genomic era, PHI data have been produced in large-scale within the last decade. Systems biology-based methods for the inference and analysis of PHI regulatory, metabolic, and protein-protein networks to shed light on infection mechanisms are gaining increasing demand thanks to the availability of omics data. The knowledge derived from the PHIs may largely contribute to the identification of new and more efficient therapeutics to prevent or cure infections. There are recent efforts for the detailed documentation of these experimentally verified PHI data through Web-based databases and platforms. Despite these advances in data archiving, there are still large amounts of PHI data in the biomedical literature yet to be discovered and novel text mining methods are in development to unearth such hidden data. Here, we review a collection of recent studies on computational systems biology of PHIs with a special focus on the methods for the inference and analysis of PHI networks, covering also the Web-based databases and text-mining efforts to unravel the data hidden in the literature.
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The time-interval between infections and viral hierarchies are determinants of viral interference following influenza virus infection in a ferret model

The time-interval between infections and viral hierarchies are determinants of viral interference following influenza virus infection in a ferret model | Viral Modeling and Simulation | Scoop.it

Background. Epidemiological studies suggest that following infection with influenza virus, there is a short period during which a host experiences a lower susceptibility to infection with other influenza viruses. This viral interference appears to be independent of any antigenic similarities between the two viruses. We used the ferret model of human influenza to systematically investigate viral interference.

Methods. Ferrets were first infected then challenged 1 to 14 days later with pairs of influenza A(H1N1)pdm09, A(H3N2) and influenza B viruses circulating in 2009 and 2010.

Results. Viral interference was observed when the time-interval between initiation of primary infection and subsequent challenge was less than one week. This effect was virus-specific and occurred between antigenically related and unrelated viruses. Co-infections occurred when 1 or 3 days separated infections. Ongoing shedding from the primary virus infection was associated with viral interference after the secondary challenge.

Conclusion. The time-interval between infections and the sequential combination of viruses presented were important determinants of the degree of viral interference. The influenza viruses in this study appear to have an ordered hierarchy according to their ability to block or delay infection, which may contribute to the dominance of different viruses often seen in an influenza season.

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Frontiers | Of mice, macaques and men: scaling of virus dynamics and immune responses | Virology

In this Opinion piece, I argue that the dynamics of viruses and the cellular immune response depend on the body size of the host. I use allometric scaling theory to interpret observed quantitative differences in the infection dynamics of lymphocytic choriomeningitis virus (LCMV) in mice (Mus musculus), simian immunodeficiency virus (SIV) in rhesus macaques (Macaca mulatta) and human immunodeficiency virus (HIV) in humans.

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Global Transcriptome Analysis in Influenza-Infected Mouse Lungs Reveals the Kinetics of Innate and Adaptive Host Immune Responses

Global Transcriptome Analysis in Influenza-Infected Mouse Lungs Reveals the Kinetics of Innate and Adaptive Host Immune Responses | Viral Modeling and Simulation | Scoop.it
An infection represents a highly dynamic process involving complex biological responses of the host at many levels. To describe such processes at a global level, we recorded gene expression changes in mouse lungs after a non-lethal infection with influenza A virus over a period of 60 days. Global analysis of the large data set identified distinct phases of the host response. The increase in interferon genes and up-regulation of a defined NK-specific gene set revealed the initiation of the early
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an older article that I missed but looks good

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Ebola virus infection modeling and identifiability problems | Frontiers | Infectious Diseases

The recent outbreaks of Ebola virus (EBOV) infections have underlined the impact of the virus as a major threat for human health. Due to the high biosafety classification of EBOV (level 4), basic research is very limited. Therefore, the development of new avenues of thinking to advance quantitative comprehension of the virus and its interaction with the host cells is urgently needed to tackle this lethal disease. Mathematical modelling of the EBOV dynamics can be instrumental to interpret Ebola infection kinetics on quantitative grounds. To the best of our knowledge, a mathematical modelling approach to unravel the interaction between EBOV and the host cells is still missing. In this paper, a mathematical model based on differential equations is used to represent the basic interactions between EBOV and wild-type Vero cells in vitro. Parameter sets that represent infectivity of pathogens are estimated for EBOV infection and compared with influenza virus infection kinetics. The average infecting time of wild-type Vero cells in EBOV is slower than in influenza infection. Simulation results suggest that the slow infecting time of EBOV could be compensated by its efficient replication. This study reveals several identifiability problems and what kind of experiments are necessary to advance the quantification of EBOV infection. A first mathematical approach of EBOV dynamics and the estimation of standard parameters in viral infections kinetics is the key contribution of this work, paving the way for future modelling work on EBOV infection.
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Modeling infectious disease dynamics in the complex landscape of global health - Science

Despite some notable successes in the control of infectious diseases, transmissible pathogens still pose an enormous threat to human and animal health. The ecological and evolutionary dynamics of infections play out on a wide range of interconnected temporal, organizational, and spatial scales, which span hours to months, cells to ecosystems, and local to global spread. Moreover, some pathogens are directly transmitted between individuals of a single species, whereas others circulate among multiple hosts, need arthropod vectors, or can survive in environmental reservoirs. Many factors, including increasing antimicrobial resistance, increased human connectivity and changeable human behavior, elevate prevention and control from matters of national policy to international challenge. In the face of this complexity, mathematical models offer valuable tools for synthesizing information to understand epidemiological patterns, and for developing quantitative evidence for decision-making in global health.
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A Model formulation for the Transmission Dynamics of Avian Influenza

A Model formulation for the Transmission Dynamics of Avian Influenza | Viral Modeling and Simulation | Scoop.it
IOSR Journal of Mathematics (IOSR-JM) vol.10 issue.6 version.6
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Antibody Responses during Hepatitis B Viral Infection

Antibody Responses during Hepatitis B Viral Infection | Viral Modeling and Simulation | Scoop.it
Author Summary Hepatitis B vaccine induces life-long protection in vaccinated individuals. In the absence of vaccination, however, hepatitis B virus can cause both self-limiting and chronic disease. We investigate whether antibodies against hepatitis B play a role in virus clearance. We developed a mathematical model that describes the production of antibodies to both infectious virus and non-infectious subviral particles (with hepatitis B surface proteins, but no nucleic acids) and compared
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Stochastic modelling of viral blips in HIV-1-infected patients: Effects of inhomogeneous density fluctuations

Abstract

We propose an stochastic model of HIV-1 infection dynamics under HAART in order to analyse the origin and dynamics of the so-called viral blips, i.e. episodes of transient viremia that occur in the phase of where the disease remains in a latent state during which the viral load raises above the detection limit of standard clinical assays. Based on prior work in the subject, we consider an infection model in which latently infected cell compartment sustains a residual (latent) infection over long periods of time. Unlike previous models, we include the effects of inhomogeneities in cell and virus concentration in the blood stream. We further consider the effect of burst virion production. By comparing with the experimental results obtained during an study in which intensive sampling was carried out on HIV-1-infected patients undergoing HAART over a long period of time, we conclude that our model supports the hypothesis that viral blips are consistent with random fluctuations around the average viral load. We further observe that agreement between our simulation results and the blip statistics obtained in the aformentioned study improves when burst virion production is considered. We also study the effect of sample manipulation artifacts on the results produced by our model, in particular, that of the post-extraction handling time, i.e. the time elapsed between sample extraction and actual test. Our results support the notion that the statistics of viral blips can be critically affected by such artifacts

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Forecasting the flu: researchers look for ways to let doctors, hospitals know what lies ahead - The Boston Globe

Forecasting the flu: researchers look for ways to let doctors, hospitals know what lies ahead - The Boston Globe | Viral Modeling and Simulation | Scoop.it
Teams of researchers, including two groups in Boston, are devising ways to predict the start, peak, and end of the yearly influenza season, using techniques similar to weather forecasting.
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Pathogenic Influenza Viruses and Coronaviruses Utilize Similar and Contrasting Approaches To Control Interferon-Stimulated Gene Responses

Pathogenic Influenza Viruses and Coronaviruses Utilize Similar and Contrasting Approaches To Control Interferon-Stimulated Gene Responses | Viral Modeling and Simulation | Scoop.it

IMPORTANCE This work combines systems biology and experimental validation to identify and confirm strategies used by viruses to control the immune response. Using a novel screening approach, specific comparison between highly pathogenic influenza viruses and coronaviruses revealed similarities and differences in strategies to control the interferon and innate immune response. These findings were subsequently confirmed and explored, revealing both a common pathway of antagonism via type I interferon (IFN) delay as well as a novel avenue for control by altered histone modification. Together, the data highlight how comparative systems biology analysis can be combined with experimental validation to derive novel insights into viral pathogenesis.

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