Determination of virus burst size in vivo using a single-cycle SIV in rhesus macaques

Antiviral capacity of the early CD8 T-cell response is predictive of natural control of SIV infection: Learning in vivo dynamics using ex vivo data

While most individuals suffer progressive disease following HIV infection, a small fraction spontaneously controls the infection. Although CD8 T-cells have been implicated in this natural control, their mechanistic roles are yet to be established. Here, we combined mathematical modeling and analysis of previously published data from 16 SIV-infected macaques, of which 12 were natural controllers, to elucidate the role of CD8 T-cells in natural control. For each macaque, we considered, in addition to the canonical in vivo plasma viral load and SIV DNA data, longitudinal ex vivo measurements of the virus suppressive capacity of CD8 T-cells. Available mathematical models do not allow analysis of such combined in vivo-ex vivo datasets. We explicitly modeled the ex vivo assay, derived analytical approximations that link the ex vivo measurements with the in vivo effector function of CD8-T cells, and integrated them with an in vivo model of virus dynamics, thus developing a new learning framework that enabled the analysis. Our model fit the data well and estimated the recruitment rate and/or maximal killing rate of CD8 T-cells to be up to 2-fold higher in controllers than non-controllers (p = 0.013). Importantly, the cumulative suppressive capacity of CD8 T-cells over the first 4-6 weeks of infection was associated with virus control (Spearman's ρ = -0.51; p = 0.05). Thus, our analysis identified the early cumulative suppressive capacity of CD8 T-cells as a predictor of natural control. Furthermore, simulating a large virtual population, our model quantified the minimum capacity of this early CD8 T-cell response necessary for long-term control. Our study presents new, quantitative insights into the role of CD8 T-cells in the natural control of HIV infection and has implications for remission strategies.

Modelling Role of Protective and Nonprotective HLA Allele Inducing Different HIV Infection Outcomes

Human immunodeficiency virus (HIV) infects CD4+ cells and causes progressive immune function failure, and CD8+ cells lyse infected CD4+ cell via recognising peptide presented by human leukocyte antigens (HLA). Variations in HLA allele lead to observed different HIV infection outcomes. Within-host HIV dynamics involves virus replication within infected cells and lysing of infected cells by CD8+ cells, but how variations in HLA alleles determine different infection outcomes was far from clear. Here, we used mathematical modelling and parameter inference with a new analysis of published virus inhibition assay data to estimate CD8+ cell lysing efficiency, and found that lysing efficiency fall in the gap between low bound (0.1-0.2 day-1 (Elemans et al. in PLoS Comput Biol 8(2):e1002381, 2012)) and upper boundary (6.5-8.4 day-1 (Wick et al. in J Virol 79(21):13579-13586, 2005)). Our outcomes indicate that both lysing efficiency and viral inoculum size jointly determine observed different infection outcomes. Low lysing rate associated with non-protective HLA alleles leads to monostable viral kinetic to high viral titre and oscillatory viral kinetics. High lysing rate associated with protective HLA alleles leads monostable viral kinetic to low viral titre and bistable viral kinetics; at a specific interval of CD8+ cell counts, small viral inoculum sizes are inhibited but not large viral inoculum sizes remain infectious. Further, with CD8+ cell recruitment, HIV kinetics always exhibit oscillatory kinetics, but lysing rate is negatively correlated with range of CD8+ cell count. Our finding highlights role of HLA allele determining different infection outcomes, thereby providing a potential mechanistic explanation for observed good and bad HIV infection outcomes induced by protective HLA allele.

Microbiological and epidemiological features of respiratory syncytial virus

The properties of the main surface proteins and the viral cycle of the respiratory syncytial virus (RSV) make it an attractive pathogen from the perspective of microbiology. The virus gets its name from the manner it infects cells, which enables it to produce syncytia, which allow the virus' genetic material to move across cells without having to release viral offspring to the cellular exterior, reducing immune system identification. This causes a disease with a high impact in both children and adults over 60, which has sparked the development of several preventive interventions based on vaccines and monoclonal antibodies for both age groups. The epidemiological characteristics of this virus, which circulates in epidemics throughout the coldest months of the year and exhibits a marked genetic and antigenic drift due to its high mutation capability, must be taken into consideration while using these preventive methods. The most important microbiological and epidemiological elements of RSV are covered in this study, along with how they have affected the creation of preventive medications and their use in the future.

Modeling dynamics of acute HIV infection incorporating density-dependent cell death and multiplicity of infection

Understanding the dynamics of acute HIV infection can offer valuable insights into the early stages of viral behavior, potentially helping uncover various aspects of HIV pathogenesis. The standard viral dynamics model explains HIV viral dynamics during acute infection reasonably well. However, the model makes simplifying assumptions, neglecting some aspects of HIV infection. For instance, in the standard model, target cells are infected by a single HIV virion. Yet, cellular multiplicity of infection (MOI) may have considerable effects in pathogenesis and viral evolution. Further, when using the standard model, we take constant infected cell death rates, simplifying the dynamic immune responses. Here, we use four models-1) the standard viral dynamics model, 2) an alternate model incorporating cellular MOI, 3) a model assuming density-dependent death rate of infected cells and 4) a model combining (2) and (3)-to investigate acute infection dynamics in 43 people living with HIV very early after HIV exposure. We find that all models qualitatively describe the data, but none of the tested models is by itself the best to capture different kinds of heterogeneity. Instead, different models describe differing features of the dynamics more accurately. For example, while the standard viral dynamics model may be the most parsimonious across study participants by the corrected Akaike Information Criterion (AICc), we find that viral peaks are better explained by a model allowing for cellular MOI, using a linear regression analysis as analyzed by R2. These results suggest that heterogeneity in within-host viral dynamics cannot be captured by a single model. Depending on the specific aspect of interest, a corresponding model should be employed.

CD4+ T cells with latent HIV-1 have reduced proliferative responses to T cell receptor stimulation

The latent reservoir for HIV-1 in resting CD4+ T cells persists despite antiretroviral therapy as a barrier to cure. The antigen-driven proliferation of infected cells is a major mechanism of reservoir persistence. However, activation through the T cell antigen receptor (TCR) can induce latent proviruses, leading to viral cytopathic effects and immune clearance. In single-cell studies, we show that, relative to uninfected cells or cells with a defective provirus, CD4+ T cells with an intact provirus have a profound proliferative defect in response to TCR stimulation. Virion production was observed in only 16.5% of cultures with an intact provirus, but proliferation was reduced even when no virion production was detected. Proliferation was inversely correlated with in vivo clone size. These results may reflect the effects of previous in vivo proliferation and do not support attempts to reduce the reservoir with antiproliferative agents, which may have greater effects on normal T cell responses.

Plasma metabonomics of classical swine fever virus-infected pigs

Classical swine fever (CSF) is an infectious disease caused by Classical swine fever virus (CSFV), which is characterized by depression, high fever, extensive skin bleeding, leukopenia, anorexia, alternating constipation, and diarrhea. Hemorrhagic infarction of the spleen is the main characteristic pathological change following CSFV infection. Large-scale outbreaks of CSF are rare in China and are mainly distributed regionally. The clinical symptoms of CSF are not obvious, and show variation from typical to atypical symptoms, which makes diagnosis based on clinical symptoms and pathology challenging. In recent years, the incidence of CSF-immunized pig farms in China has increased and new CSFV gene subtypes have appeared, posing new challenges to the prevention and control of CSF in China. Changes in metabolites caused by viral infection reflect the pathogenic process. Metabonomics can reveal the trace metabolites of organisms; however, plasma metabonomics of CSFV-infected pigs have rarely been investigated. Therefore, we used an established pig CSFV infection model to study changes in plasma metabolites. The results showed significant differences in forty-five plasma metabolites at different time periods after CSFV infection in pigs, with an increase in twenty-five metabolites and a decrease in twenty metabolites. These changed metabolites were mainly attributed to the tricarboxylic acid cycle, amino acid cycle, sugar metabolism, and fat metabolism. Thirteen metabolic pathways changed significantly in CSFV-infected pigs, including tricarboxylic acid cycle, inositol phosphate metabolism, glycine, serine and threonine metabolism,lysine degradation, alanine, aspartate and glutamic acid metabolism, pantothenate and CoA biosynthesis, β-alanine metabolism, lysine degradation, arginine and proline metabolism, glycerolipid metabolism, phenylalanine metabolism, arachidonic acid metabolism, linoleic acid metabolism. Among these, changes in fatty acid biosynthesis and metabolism occurred at all time periods post-infection. These results indicate that CSFV infection in pigs could seriously alter metabolic pathways.

CD8+ T cells control SIV infection using both cytolytic effects and non-cytolytic suppression of virus production

Whether CD8+ T lymphocytes control human immunodeficiency virus infection by cytopathic or non-cytopathic mechanisms is not fully understood. Multiple studies highlighted non-cytopathic effects, but one hypothesis is that cytopathic effects of CD8+ T cells occur before viral production. Here, to examine the role of CD8+ T cells prior to virus production, we treated SIVmac251-infected macaques with an integrase inhibitor combined with a CD8-depleting antibody, or with either reagent alone. We analyzed the ensuing viral dynamics using a mathematical model that included infected cells pre- and post- viral DNA integration to compare different immune effector mechanisms. Macaques receiving the integrase inhibitor alone experienced greater viral load decays, reaching lower nadirs on treatment, than those treated also with the CD8-depleting antibody. Models including CD8+ cell-mediated reduction of viral production (non-cytolytic) were found to best explain the viral profiles across all macaques, in addition an effect in killing infected cells pre-integration (cytolytic) was supported in some of the best models. Our results suggest that CD8+ T cells have both a cytolytic effect on infected cells before viral integration, and a direct, non-cytolytic effect by suppressing viral production.

Modeling Antiretrovial Treatment to Mitigate HIV in the Brain: Impact of the Blood-Brain Barrier

Current research in Human Immunodeficiency Virus (HIV) focuses on eradicating virus reservoirs that prevent or dampen the effectiveness of antiretroviral treatment (ART). One such reservoir, the brain, reduces treatment efficacy via the blood-brain barrier (BBB), causing an obstacle to drug penetration into the brain. In this study, we develop a mathematical model to examine the impact of the BBB on ART effectiveness for mitigating brain HIV. A thorough analysis of the model allowed us to fully characterize the global threshold dynamics with the viral clearance and persistence in the brain for the basic reproduction number less than unity and greater than unity, respectively. Our model showed that the BBB has a significant role in inhibiting the effect of ART within the brain despite the effective viral load suppression in the plasma. The level of impact, however, depends on factors such as the CNS Penetration Effectiveness (CPE) score, the slope of the drug dose-response curves, the ART initiation timing, and the number of drugs in the ART protocol. These results suggest that reducing the plasma viral load to undetectable levels due to some drug regimen may not necessarily indicate undetectable levels of HIV in the brain. Thus, the effect of the BBB on viral suppression in the brain must be considered for developing proper treatment protocols against HIV infection.

Viral load: We need a new look at an old problem?

The concept of viral load was introduced in the 1980s to measure the amount of viral genetic material in a person's blood, primarily for human immunodeficiency virus (HIV). It has since become crucial for monitoring HIV infection progression and assessing the efficacy of antiretroviral therapy. However, during the coronavirus disease 2019 pandemic, the term "viral load" became widely popularized, not only for the scientific community but for the general population. Viral load plays a critical role in both clinical patient management and research, providing valuable insights for antiviral treatment strategies, vaccination efforts, and epidemiological control measures. As measuring viral load is so important, why don't researchers discuss the best way to do it? Is it simply acceptable to use raw Ct values? Relying solely on Ct values for viral load estimation can be problematic due to several reasons. First, Ct values can vary between different quantitative polymerase chain reaction assays, platforms, and laboratories, making it difficult to compare data across studies. Second, Ct values do not directly measure the quantity of viral particles in a sample and they can be influenced by various factors such as initial viral load, sample quality, and assay sensitivity. Moreover, variations in viral RNA extraction and reverse-transcription steps can further impact the accuracy of viral load estimation, emphasizing the need for careful interpretation of Ct values in viral load assessment. Interestingly, we did not observe scientific articles addressing different strategies to quantify viral load. The absence of standardized and validated methods impedes the implementation of viral load monitoring in clinical management. The variability in cell quantities within samples and the variation in viral particle numbers within infected cells further challenge accurate viral load measurement and interpretation. To advance the field and improve patient outcomes, there is an urgent need for the development and validation of tailored, standardized methods for precise viral load quantification.

Abiotic and biotic constituents of oil sands process-affected waters

The oil sands in Northern Alberta are the largest oil sands in the world, providing an important economic resource for the Canadian energy industry. The extraction of petroleum in the oil sands begins with the addition of hot water to the bituminous sediment, generating oil sands process-affected water (OSPW), which is acutely toxic to organisms. Trillions of litres of OSPW are stored on oil sands mining leased sites in man-made reservoirs called tailings ponds. As the volume of OSPW increases, concerns arise regarding the reclamation and eventual release of this water back into the environment. OSPW is composed of a complex and heterogeneous mix of components that vary based on factors such as company extraction techniques, age of the water, location, and bitumen ore quality. Therefore, the effective remediation of OSPW requires the consideration of abiotic and biotic constituents within it to understand short and long term effects of treatments used. This review summarizes selected chemicals and organisms in these waters and their interactions to provide a holistic perspective on the physiochemical and microbial dynamics underpinning OSPW .

HIV infection dynamics and viral rebound: Modeling results from humanized mice

Despite years of combined antiretroviral therapy (cART), HIV persists in infected individuals. The virus also rebounds after the cessation of cART. The sources contributing to viral persistence and rebound are not fully understood. When viral rebound occurs, what affects the time to rebound and how to delay the rebound remain unclear. In this paper, we started with the data fitting of an HIV infection model to the viral load data in treated and untreated humanized myeloid-only mice (MoM) in which macrophages serve as the target of HIV infection. By fixing the parameter values for macrophages from the MoM fitting, we fit a mathematical model including the infection of two target cell populations to the viral load data from humanized bone marrow/liver/thymus (BLT) mice, in which both CD4+ T cells and macrophages are the target of HIV infection. Data fitting suggests that the viral load decay in BLT mice under treatment has three phases. The loss of infected CD4+ T cells and macrophages is a major contributor to the first two phases of viral decay, and the last phase may be due to the latent infection of CD4+ T cells. Numerical simulations using parameter estimates from the data fitting show that the pre-ART viral load and the latent reservoir size at treatment cessation can affect viral growth rate and predict the time to viral rebound. Model simulations further reveal that early and prolonged cART can delay the viral rebound after cessation of treatment, which may have implications in the search for functional control of HIV infection.

A simple model for viral decay dynamics and the distribution of infected cell life spans in SHIV-infected infant rhesus macaques

The dynamics of HIV viral load following the initiation of antiretroviral therapy is not well-described by simple, single-phase exponential decay. Several mathematical models have been proposed to describe its more complex behavior, the most popular of which is two-phase exponential decay. The underlying assumption in two-phase exponential decay is that there are two classes of infected cells with different lifespans. However, with the exception of CD4+ T cells, there is not a consensus on all of the cell types that can become productively infected, and the fit of the two-phase exponential decay to observed data from SHIV.C.CH505 infected infant rhesus macaques was relatively poor. Therefore, we propose a new model for viral decay, inspired by the Gompertz model where the decay rate itself is a dynamic variable. We modify the Gompertz model to include a linear term that modulates the decay rate. We show that this simple model performs as well as the two-phase exponential decay model on HIV and SIV data sets, and outperforms it for the infant rhesus macaque SHIV.C.CH505 infection data set. We also show that by using a stochastic differential equation formulation, the modified Gompertz model can be interpreted as being driven by a population of infected cells with a continuous distribution of cell lifespans, and estimate this distribution for the SHIV.C.CH505-infected infant rhesus macaques. Thus, we find that the dynamics of viral decay in this model of infant HIV infection and treatment may be explained by a distribution of cell lifespans, rather than two distinct cell types.

The ratio versus difference optimization and its implications for optimality theory

Among the classical models of optimization, some models maximize the ratio of returns to investment and others maximize the difference between returns and investment. However, an understanding of under what conditions the ratio or the difference approaches are appropriate is still fragmentary. Under specific contexts, it has been stated that when the investable amount, but not the opportunity for investment, is perceived to be limiting, a ratio optimum is appropriate, whereas a difference optimum is appropriate when the opportunity for investment, but not the investable amount, is perceived to be limiting. The question is important because the strategies indicated by ratio optimum can be substantially different than the ones suggested by difference optimum. We make a general case here to examine and expand this principle and apply it to many evolutionary ecological problems including parental investment, offspring quality-quantity trade-off, nectar production, pollinator behavior viral burst size, and intracellular protein handling. We show that the ratio-difference distinction in optimization models resolves many long-standing debates and conundrums in evolution and behavior.

Cooperative Virus-Virus Interactions: An Evolutionary Perspective

Despite extensive evidence of virus-virus interactions, not much is known about their biological significance. Importantly, virus-virus interactions could have evolved as a form of cooperation or simply be a by-product of other processes. Here, we review and discuss different types of virus-virus interactions from the point of view of social evolution, which provides a well-established framework for interpreting the fitness costs and benefits of such traits. We also classify interactions according to their mechanisms of action and speculate on their evolutionary implications. As in any other biological system, the evolutionary stability of viral cooperation critically requires cheaters to be excluded from cooperative interactions. We discuss how cheater viruses exploit cooperative traits and how viral populations are able to counteract this maladaptive process.

Modeling recapitulates the heterogeneous outcomes of SARS-CoV-2 infection and quantifies the differences in the innate immune and CD8 T-cell responses between patients experiencing mild and severe symptoms

SARS-CoV-2 infection results in highly heterogeneous outcomes, from cure without symptoms to acute respiratory distress and death. Empirical evidence points to the prominent roles of innate immune and CD8 T-cell responses in determining the outcomes. However, how these immune arms act in concert to elicit the outcomes remains unclear. Here, we developed a mathematical model of within-host SARS-CoV-2 infection that incorporates the essential features of the innate immune and CD8 T-cell responses. Remarkably, by varying the strengths and timings of the two immune arms, the model recapitulated the entire spectrum of outcomes realized. Furthermore, model predictions offered plausible explanations of several confounding clinical observations, including the occurrence of multiple peaks in viral load, viral recrudescence after symptom loss, and prolonged viral positivity. We applied the model to analyze published datasets of longitudinal viral load measurements from patients exhibiting diverse outcomes. The model provided excellent fits to the data. The best-fit parameter estimates indicated a nearly 80-fold stronger innate immune response and an over 200-fold more sensitive CD8 T-cell response in patients with mild compared to severe infection. These estimates provide quantitative insights into the likely origins of the dramatic inter-patient variability in the outcomes of SARS-CoV-2 infection. The insights have implications for interventions aimed at preventing severe disease and for understanding the differences between viral variants.

Complementary Effects of Virus Population Are Required for Efficient Virus Infection

It is believed that the virions of a virus infecting a host may share the identical viral genome and characteristics. However, the role of genomic heterogeneity of the virions of a virus in virus infection has not been extensively explored. To address this issue, white spot syndrome virus (WSSV), a DNA virus infecting crustaceans, was characterized in the current study. In WSSV, differences in two nucleotides of the viral genome generated two types of WSSV, forming a virus population that consisted of Type A WSSV (encoding WSSV lncRNA-24) and Type B WSSV (encoding the wsv195 gene) at a ratio of 1:3. The virus populations in all virus-infected cells and tissues of different hosts exhibited a stable 1:3 structure. WSSV lncRNA-24 in Type A WSSV promoted virus infection by binding to shrimp and WSSV miRNAs, while the wsv195 gene in Type B WSSV played an essential role in virus infection. Loss of Type A WSSV or Type B WSSV in the WSSV population led to a 100-fold decrease in viral copy number in shrimp. Simultaneous loss of both types of WSSV prevented virus infection. These results indicated that the virus infection process was completed by two types of WSSV encoding different functional genes, revealing the complementary effects of WSSV population. Therefore, our study highlights the importance of the complementarity of virus population components in virus infection.

Evolutionary Landscapes of Host-Virus Arms Races

Vertebrate immune systems suppress viral infection using both innate restriction factors and adaptive immunity. Viruses mutate to escape these defenses, driving hosts to counterevolve to regain fitness. This cycle recurs repeatedly, resulting in an evolutionary arms race whose outcome depends on the pace and likelihood of adaptation by host and viral genes. Although viruses evolve faster than their vertebrate hosts, their proteins are subject to numerous functional constraints that impact the probability of adaptation. These constraints are globally defined by evolutionary landscapes, which describe the fitness and adaptive potential of all possible mutations. We review deep mutational scanning experiments mapping the evolutionary landscapes of both host and viral proteins engaged in arms races. For restriction factors and some broadly neutralizing antibodies, landscapes favor the host, which may help to level the evolutionary playing field against rapidly evolving viruses. We discuss the biophysical underpinnings of these landscapes and their therapeutic implications.

Estimation of the in vivo neutralization potency of eCD4Ig and conditions for AAV-mediated production for SHIV long-term remission

The engineered protein eCD4Ig has emerged as a promising approach to achieve HIV remission in the absence of antiviral therapy. eCD4Ig neutralizes nearly all HIV-1 isolates and induces antibody-dependent cell-mediated cytotoxicity (ADCC) in vitro. To characterize the in vivo antiviral neutralization and possible ADCC effects of eCD4Ig, we fit mathematical models to eCD4Ig, anti–eCD4Ig-drug antibody (ADA), and viral load kinetics from healthy and simian-human immunodeficiency virus AD8 (SHIV-AD8) infected nonhuman primates that were treated with single or sequentially dosed eCD4Ig passive administrations. Our model predicts that eCD4Ig transiently decreases SHIV viral loads due to neutralization only with an in vivo IC₅₀ of ~25 μg/ml but with limited effect due to ADA. Simulations suggest that endogenous, continuous expression of eCD4Ig at levels greater than 10⁵ μg/day, as is possible with Adeno-associated virus (AAV) vector-based production, could overcome the diminishing effects of ADA and allow for long-term remission of SHIV viremia in nonhuman primates.

The total number and mass of SARS-CoV-2 virions

Quantitatively describing the time course of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection within an infected individual is important for understanding the current global pandemic and possible ways to combat it. Here we integrate the best current knowledge about the typical viral load of SARS-CoV-2 in bodily fluids and host tissues to estimate the total number and mass of SARS-CoV-2 virions in an infected person. We estimate that each infected person carries 109 to 1011 virions during peak infection, with a total mass in the range of 1 μg to 100 μg, which curiously implies that all SARS-CoV-2 virions currently circulating within human hosts have a collective mass of only 0.1 kg to 10 kg. We combine our estimates with the available literature on host immune response and viral mutation rates to demonstrate how antibodies markedly outnumber the spike proteins, and the genetic diversity of virions in an infected host covers all possible single nucleotide substitutions.

Mechanistic basis of post-treatment control of SIV after anti-α4β7 antibody therapy

Treating macaques with an anti-α4β7 antibody under the umbrella of combination antiretroviral therapy (cART) during early SIV infection can lead to viral remission, with viral loads maintained at < 50 SIV RNA copies/ml after removal of all treatment in a subset of animals. Depletion of CD8⁺ lymphocytes in controllers resulted in transient recrudescence of viremia, suggesting that the combination of cART and anti-α4β7 antibody treatment led to a state where ongoing immune responses kept the virus undetectable in the absence of treatment. A previous mathematical model of HIV infection and cART incorporates immune effector cell responses and exhibits the property of two different viral load set-points. While the lower set-point could correspond to the attainment of long-term viral remission, attaining the higher set-point may be the result of viral rebound. Here we expand that model to include possible mechanisms of action of an anti-α4β7 antibody operating in these treated animals. We show that the model can fit the longitudinal viral load data from both IgG control and anti-α4β7 antibody treated macaques, suggesting explanations for the viral control associated with cART and an anti-α4β7 antibody treatment. This effective perturbation to the virus-host interaction can also explain observations in other nonhuman primate experiments in which cART and immunotherapy have led to post-treatment control or resetting of the viral load set-point. Interestingly, because the viral kinetics in the various treated animals differed—some animals exhibited large fluctuations in viral load after cART cessation—the model suggests that anti-α4β7 treatment could act by different primary mechanisms in different animals and still lead to post-treatment viral control. This outcome is nonetheless in accordance with a model with two stable viral load set-points, in which therapy can perturb the system from one set-point to a lower one through different biological mechanisms.

Some macaques treated with an anti-α4β7 monoclonal antibody along with antiretroviral therapy during the early stages of simian immunodeficiency virus infection had their viral load become undetectable (below 50 SIV RNA copies/ml) after all treatment was stopped, whereas animals not given the antibody all had their viral loads rebound to high levels. Using a mathematical model, we examined four potential ways in which the antibody could have altered the balance between viral growth and immune control to maintain an undetectable viral load. We show that a shift to controlled infection can occur through multiple biologically reasonable mechanisms of action of the anti-α4β7 antibody.

Modeling Intracellular Delay in Within-Host HIV Dynamics Under Conditioning of Drugs of Abuse

Drugs of abuse, such as opiates, have been widely associated with the enhancement of HIV replication, the acceleration of disease progression, and severe neuropathogenesis. Specifically, the presence of drugs of abuse (morphine) switches target cells (CD4[Formula: see text] T cells) from lower-to-higher susceptibility to HIV infection. The effect of such switching behaviors on viral dynamics may be altered due to the intracellular delay (the replication time between viral entry into a target cell and the production of new viruses by the infected cell). In this study, we develop, for the first time, a viral dynamics model that includes an intracellular delay under the conditioning of drugs of abuse. We parameterize the model using experimental data from simian immunodeficiency virus infection of morphine-addicted macaques. Results from thorough mathematical analyses and numerical simulations of our model show that the intracellular delay can play a significant role in HIV dynamics under the conditioning of drugs of abuse, particularly during the acute phase of infection. Our model and the related results provide new insights into the HIV dynamics and may help develop strategies to control HIV infections in drug abusers.

The role of syncytia during viral infections

Respiratory syncytial virus (RSV) is a common, contagious infection of the lungs and the respiratory tract. RSV is characterized by syncytia, which are multinuclear cells created by cells that have fused together. We use a mathematical model to study how different assumptions about the viral production and lifespan of syncytia change the resulting infection time course. We find that the effect of syncytia on viral titer is only apparent when the basic reproduction number for infection via syncytia formation is similar to the reproduction number for cell free viral transmission. When syncytia fusion rate is high, we find the presence of syncytia can lead to slowly growing infections if viral production is suppressed in syncytia. Our model provides insight into how the presence of syncytia can affect the time course of a viral infection.

In Situ Programming of CAR T Cells

Gene therapy makes it possible to engineer chimeric antigen receptors (CARs) to create T cells that target specific diseases. However, current approaches require elaborate and expensive protocols to manufacture engineered T cells ex vivo, putting this therapy beyond the reach of many patients who might benefit. A solution could be to program T cells in vivo. Here, we evaluate the clinical need for in situ CAR T cell programming, compare competing technologies, review current progress, and provide a perspective on the long-term impact of this emerging and rapidly flourishing biotechnology field.

The total number and mass of SARS-CoV-2 virions

Quantitatively describing the time course of the SARS-CoV-2 infection within an infected individual is important for understanding the current global pandemic and possible ways to combat it. Here we integrate the best current knowledge about the typical viral load of SARS-CoV-2 in bodily fluids and host tissues to estimate the total number and mass of SARS-CoV-2 virions in an infected person. We estimate that each infected person carries 109-1011 virions during peak infection, with a total mass in the range of 1-100 μg, which curiously implies that all SARS-CoV-2 virions currently circulating within human hosts have a collective mass of only 0.1-10 kg. We combine our estimates with the available literature on host immune response and viral mutation rates to demonstrate how antibodies markedly outnumber the spike proteins and the genetic diversity of virions in an infected host covers all possible single nucleotide substitutions.

Unified model of short- and long-term HIV viral rebound for clinical trial planning

Antiretroviral therapy (ART) effectively controls HIV infection, suppressing HIV viral loads. Typically suspension of therapy is rapidly followed by rebound of viral loads to high, pre-therapy levels. Indeed, a recent study showed that approximately 90% of treatment interruption study participants show viral rebound within at most a few months of therapy suspension, but the remaining 10%, showed viral rebound some months, or years, after ART suspension. Some may even never rebound. We investigate and compare branching process models aimed at gaining insight into these viral dynamics. Specifically, we provide a theory that explains both short- and long-term viral rebounds, and post-treatment control, via a multitype branching process with time-inhomogeneous rates, validated with data from Li et al. (Li et al. 2016 AIDS30, 343-353. (doi:10.1097/QAD.0000000000000953)). We discuss the associated biological interpretation and implications of our best-fit model. To test the effectiveness of an experimental intervention in delaying or preventing rebound, the standard practice is to suspend therapy and monitor the study participants for rebound. We close with a discussion of an important application of our modelling in the design of such clinical trials.

Patterns of virus growth across the diversity of life

Although viruses in their natural habitats add up to less than 10% of the biomass, they contribute more than 90% of the genome sequences [1]. These viral sequences or 'viromes' encode viruses that populate the Earth's oceans [2, 3] and terrestrial environments [4, 5], where their infections impact life across diverse ecological niches and scales [6, 7], including humans [8-10]. Most viruses have yet to be isolated and cultured [11-13], and surprisingly few efforts have explored what analysis of available data might reveal about their nature. Here, we compiled and analyzed seven decades of one-step growth and other data for viruses from six major families, including their infections of archaeal, bacterial and eukaryotic hosts [14-191]. We found that the use of host cell biomass for virus production was highest for archaea at 10%, followed by bacteria at 1% and eukarya at 0.01%, highlighting the degree to which viruses of archaea and bacteria exploit their host cells. For individual host cells, the yield of virus progeny spanned a relatively narrow range (10-1000 infectious particles per cell) compared with the million-fold difference in size between the smallest and largest cells. Furthermore, healthy and infected host cells were remarkably similar in the time they needed to multiply themselves or their virus progeny. Specifically, the doubling time of healthy cells and the delay time for virus release from infected cells were not only correlated (r = 0.71, p < 10-10, n = 101); they also spanned the same range from tens of minutes to about a week. These results have implications for better understanding the growth, spread and persistence of viruses in complex natural habitats that abound with diverse hosts, including humans and their associated microbes.

The within-host fitness of HIV-1 increases with age in ART-naïve HIV-1 subtype C infected children

As the immune system develops with age, children combat infections better. HIV-1, however, targets an activated immune system, potentially rendering children increasingly permissive to HIV-1 infection as they grow. How HIV-1 fitness changes with age in children is unknown. Here, we estimated the within-host basic reproductive ratio, R₀, a marker of viral fitness, in HIV-1 subtype C-infected children in India, aged between 84 days and 17 years. We measured serial viral load and CD4 T cell counts in 171 children who initiated first-line ART. For 25 children, regular and frequent measurements provided adequate data points for analysis using a mathematical model of viral dynamics to estimate R₀. For the rest, we used CD4 counts for approximate estimation of R₀. The viral load decline during therapy was biphasic. The mean lifespans of productively and long-lived infected cells were 1.4 and 27.8 days, respectively. The mean R₀ was 1.5 in children aged < 5 years, increased with age, and approached 6.0 at 18 years, close to 5.8 estimated previously for adults. The tolerogenic immune environment thus compromises HIV-1 fitness in young children. Early treatment initiation, when the R₀ is small, will likely improve viral control, in addition to suppressing the latent reservoir.

Modeling HIV-1 infection in the brain

While highly active antiretroviral therapy (HAART) is successful in controlling the replication of Human Immunodeficiency Virus (HIV-1) in many patients, currently there is no cure for HIV-1, presumably due to the presence of reservoirs of the virus. One of the least studied viral reservoirs is the brain, which the virus enters by crossing the blood-brain barrier (BBB) via macrophages, which are considered as conduits between the blood and the brain. The presence of HIV-1 in the brain often leads to HIV associated neurocognitive disorders (HAND), such as encephalitis and early-onset dementia. In this study we develop a novel mathematical model that describes HIV-1 infection in the brain and in the plasma coupled via the BBB. The model predictions are consistent with data from macaques infected with a mixture of simian immunodeficiency virus (SIV) and simian-human immunodeficiency virus (SHIV). Using our model, we estimate the rate of virus transport across the BBB as well as viral replication inside the brain, and we compute the basic reproduction number. We also carry out thorough sensitivity analysis to define the robustness of the model predictions on virus dynamics inside the brain. Our model provides useful insight into virus replication within the brain and suggests that the brain can be an important reservoir causing long-term viral persistence.

Quantifying antiviral effects against simian/human immunodeficiency virus induced by host immune response

Chimeric simian and human immunodeficiency viruses (SHIVs) are appropriate animal models for the human immunodeficiency virus (HIV) because HIV has quite a narrow host range. Additionally, SHIVs that encode the HIV-1 Env protein and are infectious to macaques have many strains that show different pathogenesis, such as the highly pathogenic SHIV-KS661 and the less pathogenic SHIV-#64. Therefore, we used SHIVs to understand different aspects of AIDS pathogenesis. In a previous study, we established a mathematical model of in vivo early SHIV infection dynamics, which revealed the expected uninfected and infected dynamics in Rhesus macaques. In concrete, the number of uninfected CD4⁺ T cells in SHIV-KS661-infected Rhesus macaques decreased more significantly and rapidly than that of SHIV-#64 Rhesus macaques, and these Rhesus macaques did not any induce host immune response. In contrast, the number of uninfected CD4⁺ T cells in SHIV-#64-infected Rhesus macaques is maintained, and host immune response developed. Although we considered that the peak viral load might determine whether systemic CD4⁺ T cell depletion occurs or host immune responses develop, we could not investigate this because our model quantified only SHIV infection prior to the development of the pathogenicity or host immune responses. Therefore, we developed a new mathematical model to investigate why SHIV-#64 and SHIV-KS661 showed different long-term viral dynamics. We fitted our new model considering antibody responses to our experimental datasets that included antibody titers, CD4⁺ T cells, and viral load data. We performed a maximum likelihood estimation using a non-linear mixed effect model. From the results, we derived the basic reproduction numbers of SHIV-#64 and SHIV-KS661 from intravenous infection (IV) and SHIV-KS661 from intrarectal infection (IR), as well as the antiviral effects of antibodies against SHIV-#64(IV) and SHIV-KS661(IR). We found significant differences between the basic reproduction number of SHIV-#64(IV) or -KS661(IR) and that of SHIV-KS661(IV). We found no clear difference between the antiviral effects of SHIV-#64(IV) and SHIV-KS661(IR), and revealed that an antiviral effect more than 90% of that of maximum antibody responses was induced from initial antibody responses (i.e., antibody response just after its inducement). In conclusion, we found that the basic reproduction number, rather than SHIV strains determines whether systemic CD4⁺ T cell depletion develops, and the subsequent antibody responses occurs.

Cells producing residual viremia during antiretroviral treatment appear to contribute to rebound viremia following interruption of treatment

During antiretroviral therapy (ART) that suppresses HIV replication to below the limit-of-quantification, virions produced during ART can be detected at low frequencies in the plasma, termed residual viremia (RV). We hypothesized that a reservoir of HIV-infected cells actively produce and release virions during ART that are potentially infectious, and that following ART-interruption, these virions can complete full-cycles of replication and contribute to rebound viremia. Therefore, we studied the dynamics of RV sequence variants in 3 participants who initiated ART after ~3 years of infection and were ART-suppressed for >6 years prior to self-initiated ART-interruptions. Longitudinal RV C2V5env sequences were compared to sequences from pre-ART plasma, supernatants of quantitative viral outgrowth assays (QVOA) of cells collected during ART, post-ART-interruption plasma, and ART-re-suppression plasma. Identical, “putatively clonal,” RV sequences comprised 8–84% of sequences from each timepoint. The majority of RV sequences were genetically similar to those from plasma collected just prior to ART-initiation, but as the duration of ART-suppression increased, an increasing proportion of RV variants were similar to sequences from earlier in infection. Identical sequences were detected in RV over a median of 3 years (range: 0.3–8.2) of ART-suppression. RV sequences were identical to pre-ART plasma viruses (5%), infectious viruses induced in QVOA (4%) and rebound viruses (5%) (total n = 21/154 (14%) across the 3 participants). RV sequences identical to ART-interruption “rebound” sequences were detected 0.1–7.4 years prior to ART-interruption. RV variant prevalence and persistence were not associated with detection of the variant among rebound sequences. Shortly after ART-re-suppression, variants that had been replicating during ART-interruptions were detected as RV (n = 5). These studies show a dynamic, virion-producing HIV reservoir that contributes to rekindling infection upon ART-interruption. The persistence of identical RV variants over years suggests that a subpopulation of HIV-infected clones frequently or continuously produce virions that may resist immune clearance; this suggests that cure strategies should target this active as well as latent reservoirs.

HIV-infected individuals receiving effective antiretroviral treatment (ART) produce virions detected in the blood at very low levels, termed residual viremia (RV). To understand the significance of RV as related to the persistence of HIV infection, we characterized the dynamics of RV sequence variants among plasma viruses over nearly a decade of ART and assessed whether RV contributed to rekindling viremia upon ART-interruption. The HIV reservoir producing RV appeared to be “seeded” at various times before ART-initiation. Identical RV sequences likely produced by a clonal cell population, varied over time, with unique sequence variants persisting over a median of 3 years. A subset of RV variants (14%) were identical to viruses found in pre-ART plasma, infectious viruses induced from cultured CD4⁺ T blood lymphocytes collected during ART, or in rebound plasma during ART-interruption. The persistence of unique RV variants over years, infers that the clones of HIV-infected cells producing these virions resist immune clearance or a subset of these clones are activated on a rolling basis, and that novel treatment strategies are needed to target this active reservoir that contributes to viral rebound.

Comparison of empirical and dynamic models for HIV viral load rebound after treatment interruption

OBJECTIVE

To compare empirical and mechanistic modeling approaches for describing HIV-1 RNA viral load trajectories after antiretroviral treatment interruption and for identifying factors that predict features of viral rebound process.

METHODS

We apply and compare two modeling approaches in analysis of data from 346 participants in six AIDS Clinical Trial Group studies. From each separate analysis, we identify predictors for viral set points and delay in rebound. Our empirical model postulates a parametric functional form whose parameters represent different features of the viral rebound process, such as rate of rise and viral load set point. The viral dynamics model augments standard HIV dynamics models-a class of mathematical models based on differential equations describing biological mechanisms-by including reactivation of latently infected cells and adaptive immune response. We use Monolix, which makes use of a Stochastic Approximation of the Expectation-Maximization algorithm, to fit non-linear mixed effects models incorporating observations that were below the assay limit of quantification.

RESULTS

Among the 346 participants, the median age at treatment interruption was 42. Ninety-three percent of participants were male and sixty-five percent, white non-Hispanic. Both models provided a reasonable fit to the data and can accommodate atypical viral load trajectories. The median set points obtained from two approaches were similar: 4.44 log10 copies/mL from the empirical model and 4.59 log10 copies/mL from the viral dynamics model. Both models revealed that higher nadir CD4 cell counts and ART initiation during acute/recent phase were associated with lower viral set points and identified receiving a non-nucleoside reverse transcriptase inhibitor (NNRTI)-based pre-ATI regimen as a predictor for a delay in rebound.

CONCLUSION

Although based on different sets of assumptions, both models lead to similar conclusions regarding features of viral rebound process.

Early exposure to broadly neutralizing antibodies may trigger a dynamical switch from progressive disease to lasting control of SHIV infection

Antiretroviral therapy (ART) for HIV-1 infection is life-long. Stopping therapy typically leads to the reignition of infection and progressive disease. In a major breakthrough, recent studies have shown that early initiation of ART can lead to sustained post-treatment control of viremia, raising hopes of long-term HIV-1 remission. ART, however, elicits post-treatment control in a small fraction of individuals treated. Strikingly, passive immunization with broadly neutralizing antibodies (bNAbs) of HIV-1 early in infection was found recently to elicit long-term control in a majority of SHIV-infected macaques, suggesting that HIV-1 remission may be more widely achievable. The mechanisms underlying the control elicited by bNAb therapy, however, remain unclear. Untreated infection typically leads to progressive disease. We hypothesized that viremic control represents an alternative but rarely realized outcome of the infection and that early bNAb therapy triggers a dynamical switch to this outcome. To test this hypothesis, we constructed a model of viral dynamics with bNAb therapy and applied it to analyse clinical data. The model fit quantitatively the complex longitudinal viral load data from macaques that achieved lasting control. The model predicted, consistently with our hypothesis, that the underlying system exhibited bistability, indicating two potential outcomes of infection. The first had high viremia, weak cytotoxic effector responses, and high effector exhaustion, marking progressive disease. The second had low viremia, strong effector responses, and low effector exhaustion, indicating lasting viremic control. Further, model predictions suggest that early bNAb therapy elicited lasting control via pleiotropic effects. bNAb therapy lowers viremia, which would also limit immune exhaustion. Simultaneously, it can improve effector stimulation via cross-presentation. Consequently, viremia may resurge post-therapy, but would encounter a primed effector population and eventually get controlled. ART suppresses viremia but does not enhance effector stimulation, explaining its limited ability to elicit post-treatment control relative to bNAb therapy.

In a remarkable advance in HIV cure research, a recent study showed that 3 weekly doses of HIV-1 broadly neutralizing antibodies (bNAbs) soon after infection kept viral levels controlled for years in most macaques treated. If translated to humans, this bNAb therapy may elicit a functional cure, or long-term remission, of HIV-1 infection, eliminating the need for life-long antiretroviral therapy (ART). How early bNAb therapy works remains unknown. Here, we elucidate the mechanism using mathematical modeling and analysis of in vivo data. We predict that early bNAb therapy suppresses viremia, which reduces exhaustion of cytotoxic effector cells, and enhances antigen uptake and effector stimulation. Collectively, these effects drive infection to lasting control. Model predictions based on these effects fit in vivo data quantitatively. ART controls viremia but does not improve effector stimulation, explaining its weaker ability to induce lasting control post-treatment. Our findings may help improve strategies for achieving functional cure of HIV-1 infection.

Host-virus evolutionary dynamics with specialist and generalist infection strategies: Bifurcations, bistability, and chaos

In this work, we have investigated the evolutionary dynamics of a generalist pathogen, e.g., a virus population, that evolves toward specialization in an environment with multiple host types. We have particularly explored under which conditions generalist viral strains may rise in frequency and coexist with specialist strains or even dominate the population. By means of a nonlinear mathematical model and bifurcation analysis, we have determined the theoretical conditions for stability of nine identified equilibria and provided biological interpretation in terms of the infection rates for the viral specialist and generalist strains. By means of a stability diagram, we identified stable fixed points and stable periodic orbits, as well as regions of bistability. For arbitrary biologically feasible initial population sizes, the probability of evolving toward stable solutions is obtained for each point of the analyzed parameter space. This probability map shows combinations of infection rates of the generalist and specialist strains that might lead to equal chances for each type becoming the dominant strategy. Furthermore, we have identified infection rates for which the model predicts the onset of chaotic dynamics. Several degenerate Bogdanov-Takens and zero-Hopf bifurcations are detected along with generalized Hopf and zero-Hopf bifurcations. This manuscript provides additional insights into the dynamical complexity of host-pathogen evolution toward different infection strategies.

Environmental Restrictions: A New Concept Governing HIV-1 Spread Emerging from Integrated Experimental-Computational Analysis of Tissue-Like 3D Cultures

HIV-1 can use cell-free and cell-associated transmission modes to infect new target cells, but how the virus spreads in the infected host remains to be determined. We recently established 3D collagen cultures to study HIV-1 spread in tissue-like environments and applied iterative cycles of experimentation and computation to develop a first in silico model to describe the dynamics of HIV-1 spread in complex tissue. These analyses (i) revealed that 3D collagen environments restrict cell-free HIV-1 infection but promote cell-associated virus transmission and (ii) defined that cell densities in tissue dictate the efficacy of these transmission modes for virus spread. In this review, we discuss, in the context of the current literature, the implications of this study for our understanding of HIV-1 spread in vivo, which aspects of in vivo physiology this integrated experimental-computational analysis takes into account, and how it can be further improved experimentally and in silico.

Simulating the entire natural course of HIV infection by extending the basic viral dynamics equations to include declining viral clearance

The basic model of viral dynamics is a relatively simple set of equations describing the most essential features of the host-pathogen interactions. Coupled with data, it has been used extensively and successfully to reproduce and explain the features of the early acute phase of HIV infection and the effects of antiretroviral treatment, as well as to estimate the lifespan of infected cells, viral growth and clearance rates and predict early outcomes under different circumstances. However, it cannot reproduce the entire natural course of untreated HIV infection consistently with constant parameters. Here we show that it is possible to qualitatively reproduce the whole course of untreated HIV infection within the general framework of the basic model by assuming progressively declining viral clearance coupled with viral load. We discuss the interpretation of this model as proof-of-concept that may inspire further research into the role of viral clearance in HIV infection.

Mathematical modeling to reveal breakthrough mechanisms in the HIV Antibody Mediated Prevention (AMP) trials

The ongoing Antibody Mediated Prevention (AMP) trials will uncover whether passive infusion of the broadly neutralizing antibody (bNAb) VRC01 can protect against HIV acquisition. Previous statistical simulations indicate these trials may be partially protective. In that case, it will be crucial to identify the mechanism of breakthrough infections. To that end, we developed a mathematical modeling framework to simulate the AMP trials and infer the breakthrough mechanisms using measurable trial outcomes. This framework combines viral dynamics with antibody pharmacokinetics and pharmacodynamics, and will be generally applicable to forthcoming bNAb prevention trials. We fit our model to human viral load data (RV217). Then, we incorporated VRC01 neutralization using serum pharmacokinetics (HVTN 104) and in vitro pharmacodynamics (LANL CATNAP database). We systematically explored trial outcomes by reducing in vivo potency and varying the distribution of sensitivity to VRC01 in circulating strains. We found trial outcomes could be used in a clinical trial regression model (CTRM) to reveal whether partially protective trials were caused by large fractions of VRC01-resistant (IC50>50 μg/mL) circulating strains or rather a global reduction in VRC01 potency against all strains. The former mechanism suggests the need to enhance neutralizing antibody breadth; the latter suggests the need to enhance VRC01 delivery and/or in vivo binding. We will apply the clinical trial regression model to data from the completed trials to help optimize future approaches for passive delivery of anti-HIV neutralizing antibodies.

Infusions of broadly neutralizing antibodies are currently being tested as a novel HIV prevention modality. To help interpret the results of these antibody mediated prevention (AMP) studies we developed a mathematical modeling framework. The approach combines antibody potency and drug levels with models of HIV viral dynamics, which will be generally applicable to future studies. Through simulating these clinical trials, we found trial outcomes can be used in combination to infer whether breakthrough infections are caused by large fractions of antibody-resistant circulating strains or some reduction in potency against all strains. This distinction helps to focus future trials on enhancing neutralizing antibody breadth or antibody delivery and/or in vivo binding.

Interference with HIV infection of the first cell is essential for viral clearance at sub-optimal levels of drug inhibition

HIV infection can be cleared with antiretroviral drugs if they are administered before exposure, where exposure occurs at low viral doses which infect one or few cells. However, infection clearance does not happen once infection is established, and this may be because of the very early formation of a reservoir of latently infected cells. Here we investigated whether initial low dose infection could be cleared with sub-optimal drug inhibition which allows ongoing viral replication, and hence does not require latency for viral persistence. We derived a model for infection clearance with inputs being drug effects on ongoing viral replication and initial number of infected cells. We experimentally tested the model by inhibiting low dose infection with the drug tenofovir, which interferes with initial infection, and atazanavir, which reduces the cellular virion burst size and hence inhibits replication only after initial infection. Drugs were used at concentrations which allowed infection to expand. Under these conditions, tenofovir dramatically increased clearance while atazanavir did not. Addition of latency to the model resulted in a minor decrease in clearance probability if the drug inhibited initial infection. If not, latency strongly decreased clearance even at low latent cell frequencies. Therefore, the ability of drugs to clear initial but not established infection can be recapitulated without latency and depends only on the ability to target initial infection. The presence of latency can dramatically decrease infection clearance, but only if the drug is unable to interfere with infection of the first cells.

A feature of viral infections such as HIV is that successful transmission occurs with low probability and is preventable by administration of drugs before exposure to the virus. Yet, once established, the infection is difficult or impossible to eradicate within its host. In the case of HIV, this may be explained by the establishment of a latent reservoir of infected cells insensitive to antiretroviral drugs. Here we use a combined modelling and experimental approach to determine whether low dose HIV infection can be cleared at drug concentrations which allow the expansion of HIV infection once established. We show that such sub-optimal drug levels are effective at clearing infection, provided they target the virus before it infects the first set of cells. The difference in the effect of drugs before and after the initial cells are infected does not require the establishment of viral latency. Rather, it is a quantitative effect, where the low infection dose can be cleared before amplifying viral numbers by infecting the first cells.

Effect of infectious bursal disease virus infection on energy metabolism in embryonic chicken livers

1. The purpose of this study was to investigate ATP levels and the activities of important enzymes involved in glycolysis and TCA cycle in livers of embryonated chicken eggs infected by infectious bursal disease virus (IBDV).2. Embryonated chicken eggs (9 days) were randomly divided into two groups (50 eggs per group). The first group was inoculated with a very virulent IBDV (vvIBDV) isolate into the chorioallantoic membrane. The second group was maintained as uninfected control eggs and inoculated with physiological saline. Embryo survival was assessed daily, and six embryos were sacrificed at 24, 48, 72, 96, and 120 hpi for examining livers. Viral loads in the livers were evaluated by qRT-PCR. A comparative analysis of markers associated with the regulation of energy metabolism across several functional classes (ATP, pyruvic and lactic acids, mitochondrial protein, NAD+/NADH ratios, and enolase, lactic acid dehydrogenase and the respiratory chain complex I activities) were examined in the context of IBDV infection.3. The results indicated that increases in the enzymatic activities associated with glycolytic metabolism in turn affected the synthesis and cytoplasmic concentrations of ATP at early timepoints in infected chicken embryos. Subsequently, energy metabolism was inhibited through the pathological perturbations of metabolic enzymes and mitochondrial damage, as inferred from reduced ATP generation.4. These results suggested impaired bioenergetics, which may lead to liver dysfunction consequent to IBDV infection, contributing to the disease pathogenesis.

Revealing uninfected and infected target cell dynamics from peripheral blood data in highly and less pathogenic simian/human immunodeficiency virus infected Rhesus macaque

Since chimeric simian and human immunodeficiency viruses (SHIVs) used here, that is, SHIV-#64 and -KS661 utilize both CCR5 and CXCR4 chemokine receptors, they have broad target cell properties. A highly pathogenic SHIV strain, SHIV-KS661, causes an infection that systemically depletes the CD4⁺ T cells of Rhesus macaques, while a less pathogenic strain, SHIV-#64, does not cause severe symptoms in the macaques. In our previous studies, we established in vitro quantification system for virus infection dynamics, and concluded that SHIV-KS661 effectively produces infectious virions compared with SHIV-#64 in the HSC-F cell culture. However, in vivo dynamics of SHIV infection have not been well understood. To quantify SHIV-#64 and -KS661 infection dynamics in Rhesus macaques, we developed a novel approach and analyzed total CD4⁺ T cells and viral load in peripheral blood, and reproduced the expected dynamics for the uninfected and infected CD4⁺ T cells in silico. Using our previous cell culture experimental datasets, we revealed that an infection rate constant is different between SHIV-#64 and -KS661, but the viral production rate and the death rate are similar for the both strains. Thus, here, we assumed these relations in our in vivo data and carried out the data fitting. We performed Bayesian estimation for the whole dataset using MCMC sampling, and simultaneously fitted our novel model to total CD4⁺ T cells and viral load of SHIV-#64 and -KS661 infection. Our analyses explained that the Malthusian parameter (i.e., fitness of virus infection) and the basic reproduction number (i.e., potential of virus infection) for SHIV-KS661 are significantly higher than those of SHIV-#64. In addition, we demonstrated that the number of uninfected CD4⁺ T cells in SHIV-KS661 infected Rhesus macaques decreases to the significantly lower value than that before the inoculation several days earlier compared with SHIV-#64 infection. Taken together, the differences between SHIV-#64 and -KS661 infection before the peak viral load might determine the subsequent destiny, that is, whether the systemic CD4⁺ T cell depletion occurs or the host immune response develop.

Predictions of time to HIV viral rebound following ART suspension that incorporate personal biomarkers

Antiretroviral therapy (ART) effectively controls HIV infection, suppressing HIV viral loads. Suspension of therapy is followed by rebound of viral loads to high, pre-therapy levels. However, there is significant heterogeneity in speed of rebound, with some rebounds occurring within days, weeks, or sometimes years. We present a stochastic mathematical model to gain insight into these post-treatment dynamics, specifically characterizing the dynamics of short term viral rebounds (≤ 60 days). Li et al. (2016) report that the size of the expressed HIV reservoir, i.e., cell-associated HIV RNA levels, and drug regimen correlate with the time between ART suspension and viral rebound to detectable levels. We incorporate this information and viral rebound times to parametrize our model. We then investigate insights offered by our model into the underlying dynamics of the latent reservoir. In particular, we refine previous estimates of viral recrudescence after ART interruption by accounting for heterogeneity in infection rebound dynamics, and determine a recrudescence rate of once every 2-4 days. Our parametrized model can be used to aid in design of clinical trials to study viral dynamics following analytic treatment interruption. We show how to derive informative personalized testing frequencies from our model and offer a proof-of-concept example. Our results represent first steps towards a model that can make predictions on a person living with HIV (PLWH)'s rebound time distribution based on biomarkers, and help identify PLWH with long viral rebound delays.

Modeling the Effects of Morphine-Altered Virus Specific Antibody Responses on HIV/SIV Dynamics

Drugs of abuse, such as opiates, have been widely associated with enhancing HIV replication, accelerating disease progression and diminishing host-immune responses, thereby making it harder to effectively manage HIV infection. It is thus important to study the effects of drugs of abuse on HIV-infection and immune responses. Here, we develop mathematical models that incorporate the effects of morphine-altered antibody responses on HIV/SIV dynamics. Based on fitting the model to experimental data from simian immunodeficiency virus (SIV) infections in control and morphine-addicted macaques, we found that two of the most significant effects of virus specific antibodies are neutralizing viral particles and enhancing viral clearance. Using our model, we quantified how morphine alters virus-specific antibody responses, and how this alteration affects the key components of virus dynamics such as infection rate, virus clearance, viral load, CD4⁺ T cell count, and CD4⁺ T cell loss in SIV-infected macaques under conditioning with morphine. We found that in a subpopulation of SIV-infected morphine addicted macaques, the presence of drugs of abuse may cause significantly diminished antibody responses, resulting in more severe infection with increased SIV infectivity, a decreased viral clearance rate, increased viral load, and higher CD4⁺ T cell loss.

An accurate approximation for the expected site frequency spectrum in a Galton-Watson process under an infinite sites mutation model

If viruses or other pathogens infect a single host, the outcome of infection often hinges on the fate of the initial invaders. The initial basic reproduction number R₀, the expected number of cells infected by a single infected cell, helps determine whether the initial viruses can establish a successful beachhead. To determine R₀, the Kingman coalescent or continuous-time birth-and-death process can be used to infer the rate of exponential growth in an historical population. Given M sequences sampled in the present, the two models can make the inference from the site frequency spectrum (SFS), the count of mutations that appear in exactly k sequences (k=1,2,…,M). In the case of viruses, however, if R₀ is large and an infected cell bursts while propagating virus, the two models are suspect, because they are Markovian with only binary branching. Accordingly, this article develops an approximation for the SFS of a discrete-time branching process with synchronous generations (i.e., a Galton-Watson process). When evaluated in simulations with an asynchronous, non-Markovian model (a Bellman-Harris process) with parameters intended to mimic the bursting viral reproduction of HIV, the approximation proved superior to approximations derived from the Kingman coalescent or continuous-time birth-and-death process. This article demonstrates that in analogy to methods in human genetics, the SFS of viral sequences sampled well after latent infection can remain informative about the initial R₀. Thus, it suggests the utility of analyzing the SFS of sequences derived from patient and animal trials of viral therapies, because in some cases, the initial R₀ may be able to indicate subtle therapeutic progress, even in the absence of statistically significant differences in the infection of treatment and control groups.

Low-Fidelity Assembly of Influenza A Virus Promotes Escape from Host Cells

Influenza viruses inhabit a wide range of host environments using a limited repertoire of protein components. Unlike viruses with stereotyped shapes, influenza produces virions with significant morphological variability even within clonal populations. Whether this tendency to form pleiomorphic virions is coupled to compositional heterogeneity and whether it affects replicative fitness remains unclear. Here, we address these questions by developing a strain of influenza A virus amenable to rapid compositional characterization through quantitative, site-specific labeling of viral proteins. Using this strain, we find that influenza A produces virions with broad variations in size and composition from even single infected cells. This phenotypic variability contributes to virus survival during environmental challenges, including exposure to antivirals. Complementing genetic adaptations that act over larger populations and longer times, this "low-fidelity" assembly of influenza A virus allows small populations to survive environments that fluctuate over individual replication cycles.

Early HIV infection predictions: role of viral replication errors

In order to prevent and/or control infections it is necessary to understand their early-time dynamics. However this is precisely the phase of HIV about which the least is known. To investigate the initial stages of HIV infection within a host we have developed a multi-type, continuous-time branching process model. This model is a stochastic extension of the standard viral dynamics model, under the assumption that the number of cell targets for viral infection is constant, biologically reasonable since, during the earliest stages of HIV infection, very few cells are infected relative to their total population size. We use our model to investigate three important clinical characteristics of early HIV infection following intravenous challenge: risk of infection, time to infection clearance (assuming failed infection), and time to infection detection. Our focus is on the impact of errors in viral replication that result in non-infectious virus production on these characteristics. Only a small fraction of circulating virus in any chronically infected individual is capable of infecting susceptible cells: estimates range from 1/10⁴ - 1/10³. Characterization and quantification of the processes by which virus becomes defective remains incomplete. We consider two mechanisms that result in defective virus: (1) Copying errors, i.e., lethal errors in reverse transcription, which introduce mutations into the HIV-1 proviral genome, some of which may cripple the viral genome produced, and (2) Packaging errors, i.e., errors during viral packaging, at the end of the viral replication cycle, which cause defective virus by packaging new virions without, for example, viral RNA or key proteins required for infectivity. We show that assumptions on mechanisms of defective virus production can significantly impact early HIV infection model predictions. For example, the risk of infection is orders of magnitude higher if all defective virus is associated with packaging errors, but infection is predicted to be detectable sooner following HIV exposure if all defective virus is associated with copying errors. Thus, in order to make reliable predictions of risk, clearance time, and detection time, better characterization of viral replication is required.

Estimating Initial Viral Levels during Simian Immunodeficiency Virus/Human Immunodeficiency Virus Reactivation from Latency

Human immunodeficiency virus (HIV) viremia rebounds rapidly after treatment interruption, and a variety of strategies are being explored to reduce or control viral reactivation posttreatment. This viral rebound arises from reactivation of individual latently infected cells, which spread during ongoing rounds of productive infection. The level of virus produced by the initial individual reactivating cells is not known, although it may have major implications for the ability of different immune interventions to control viral rebound. Here we use data from both HIV and simian immunodeficiency virus (SIV) treatment interruption studies to estimate the initial viral load postinterruption and thereby the initial individual reactivation event. Using a barcoded virus (SIVmac239M) to track reactivation from individual latent cells, we use the observed viral growth rates and frequency of reactivation to model the dynamics of reactivation to estimate that a single reactivated latent cell can produce an average viral load equivalent to ∼0.1 to 0.5 viral RNA (vRNA) copies/ml. Modeling of treatment interruption in HIV suggests an initial viral load equivalent of ∼0.6 to 1 vRNA copies/ml. These low viral loads immediately following latent cell reactivation provide a window of opportunity for viral control by host immunity, before further replication allows viral spread. This work shows the initial levels of viral production that must be controlled in order to successfully suppress HIV reactivation following treatment interruption.IMPORTANCE Current treatment for HIV is able to suppress viral replication and prevent disease progression. However, treatment cannot eradicate infection, because the virus lies silent within latently infected cells. If treatment is stopped, the virus usually rebounds above the level of detection within a few weeks. There are a number of approaches being tested aimed at either eradicating latently infected cells or controlling the virus if it returns. Studying both the small pool of latently infected cells and the early events during viral reactivation is difficult, because these involve very small levels of virus that are difficult to measure directly. Here, we combine experimental data and mathematical modeling to understand the very early events during viral reactivation from latency in both HIV infection of humans and SIV infection of monkeys. We find that the initial levels of virus are low, which may help in designing therapies to control early viral reactivation.

HIV Persistence on Antiretroviral Therapy and Barriers to a Cure

HIV persists within the body despite successful suppression of virus replication with antiretroviral therapy (ART). HIV lurks in latent and active reservoirs, leading to rebound of virus spread if ART is interrupted. The latent HIV reservoir is a natural consequence of the life cycle of HIV, with integration of HIV into the genomes of cells that are or later enter the resting state, resulting in transcriptionally quiescent provirus. Resting CD4 T cells comprise the majority of the latent reservoir, although new evidence points to additional, smaller cellular reservoirs of latent HIV. An alternate, so-called active reservoir of HIV also exists within cells such as those found the B cell follicle of lymph nodes, where expression of HIV RNA can be found, again despite the full suppression of viremia and viral replication. Multiple factors such as the degree of virus exposure, timing of ART, and host factors can influence the size and characteristics of the HIV reservoir. Constructing effective strategies for HIV eradication and measuring their impact will require a sophisticated knowledge of the HIV reservoir.

On the Death Rate of Abortively Infected Cells: Estimation from Simian-Human Immunodeficiency Virus Infection

Progressive T cell depletion during chronic human immunodeficiency virus type 1 (HIV) infection is a key mechanism that leads to the development of AIDS. Recent studies have suggested that most T cells in the tissue die through pyroptosis triggered by abortive infection, i.e., infection of resting T cells in which HIV failed to complete reverse transcription. However, the contribution of abortive infection to T cell loss and how quickly abortively infected cells die in vivo, key parameters for a quantitative understanding of T cell population dynamics, are not clear. Here, we infected rhesus macaques with simian-human immunodeficiency viruses (SHIV) and followed the dynamics of both plasma SHIV RNA and total cell-associated SHIV DNA. Fitting mathematical models to the data, we estimate that upon infection a majority of CD4⁺ T cells (approximately 65%, on average) become abortively infected and die at a relatively high rate of 0.27 day^-1 (half-life, 2.6 days). This confirms the importance of abortive infection in driving T cell depletion. Further, we find evidence suggesting that an immune response may be restricting viral infection 1 to 3 weeks after infection. Our study serves as a step forward toward a quantitative understanding of the mechanisms driving T cell depletion during HIV infection.IMPORTANCE In HIV-infected patients, progressive CD4⁺ T cell loss ultimately leads to the development of AIDS. The mechanisms underlying this T cell loss are not clear. Recent experimental data suggest that the majority of CD4⁺ T cells in tissue die through abortive infection, where the accumulation of incomplete HIV transcripts triggers cell death. To investigate the role of abortive infection in driving CD4⁺ T cell loss in vivo, we infected macaques with simian-human immunodeficiency viruses (SHIV) and followed the viral kinetics of both plasma RNA and cell-associated DNA during infection. Fitting mathematical models, we estimated that a large fraction of infected cells dies through abortive infection and has a half-life of approximately 2.6 days. Our results provide the first in vivo quantitative estimates of parameters characterizing abortive infection and support the notion that abortive infection represents an important mechanism underlying progressive CD4⁺ T cell depletion in vivo.

Phenotypic Differences between Asian and African Lineage Zika Viruses in Human Neural Progenitor Cells

Recent Zika virus (ZIKV) infections have been associated with a range of neurological complications, in particular congenital microcephaly. Human neural progenitor cells (hNPCs) are thought to play an important role in the pathogenesis of microcephaly, and experimental ZIKV infection of hNPCs has been shown to induce cell death. However, the infection efficiency and rate of cell death have varied between studies, which might be related to intrinsic differences between African and Asian lineage ZIKV strains. Therefore, we determined the replication kinetics, including infection efficiency, burst size, and ability to induce cell death, of two Asian and two African ZIKV strains. African ZIKV strains replicated to higher titers in Vero cells, human glioblastoma (U87MG) cells, human neuroblastoma (SK-N-SH) cells, and hNPCs than Asian ZIKV strains. Furthermore, infection with Asian ZIKV strains did not result in significant cell death early after infection, whereas infection with African ZIKV strains resulted in high percentages of cell death in hNPCs. The differences between African and Asian lineage ZIKV strains highlight the importance of including relevant ZIKV strains to study the pathogenesis of congenital microcephaly and caution against extrapolation of experimental data obtained using historical African ZIKV strains to the current outbreak. Finally, the fact that Asian ZIKV strains infect only a minority of cells with a relatively low burst size together with the lack of early cell death induction might contribute to its ability to cause chronic infections within the central nervous system (CNS). IMPORTANCE The mechanism by which ZIKV causes a range of neurological complications, especially congenital microcephaly, is not well understood. The fact that congenital microcephaly is associated with Asian lineage ZIKV strains raises the question of why this was not discovered earlier. One possible explanation is that Asian and African ZIKV strains differ in their abilities to infect cells of the CNS and to cause neurodevelopmental problems. Here, we show that Asian ZIKV strains infect and induce cell death in human neural progenitor cells-which are important target cells in the development of congenital microcephaly-less efficiently than African ZIKV strains. These features of Asian ZIKV strains likely contribute to their ability to cause chronic infections, often observed in congenital microcephaly cases. It is therefore likely that phenotypic differences between ZIKV strains could be, at least in part, responsible for the ability of Asian ZIKV strains to cause congenital microcephaly.

Autologous Stem Cell Transplantation Disrupts Adaptive Immune Responses during Rebound Simian/Human Immunodeficiency Virus Viremia

Primary HIV-1 infection induces a virus-specific adaptive/cytolytic immune response that impacts the plasma viral load set point and the rate of progression to AIDS. Combination antiretroviral therapy (cART) suppresses plasma viremia to undetectable levels that rebound upon cART treatment interruption. Following cART withdrawal, the memory component of the virus-specific adaptive immune response may improve viral control compared to primary infection. Here, using primary infection and treatment interruption data from macaques infected with simian/human immunodeficiency virus (SHIV), we observe a lower peak viral load but an unchanged viral set point during viral rebound. The addition of an autologous stem cell transplant before cART withdrawal alters viral dynamics: we found a higher rebound set point but similar peak viral loads compared to the primary infection. Mathematical modeling of the data that accounts for fundamental immune parameters achieves excellent fit to heterogeneous viral loads. Analysis of model output suggests that the rapid memory immune response following treatment interruption does not ultimately lead to better viral containment. Transplantation decreases the durability of the adaptive immune response following cART withdrawal and viral rebound. Our model's results highlight the impact of the endogenous adaptive immune response during primary SHIV infection. Moreover, because we capture adaptive immune memory and the impact of transplantation, this model will provide insight into further studies of cure strategies inspired by the Berlin patient.IMPORTANCE HIV patients who interrupt combination antiretroviral therapy (cART) eventually experience viral rebound, the return of viral loads to pretreatment levels. However, the "Berlin patient" remained free of HIV rebound over a decade after stopping cART. His cure is attributed to leukemia treatment that included an HIV-resistant stem cell transplant. Inspired by this case, we studied the impact of stem cell transplantation in a macaque simian/HIV (SHIV) system. Using a mechanistic mathematical model, we found that while primary infection generates an adaptive immune memory response, stem cell transplantation disrupts this learned immunity. The results have implications for HIV cure regimens based on stem cell transplantation.

On the duration of the period between exposure to HIV and detectable infection

HIV infection cannot be detected immediately after exposure because plasma viral loads are too small initially. The duration of this phase of infection (the "eclipse period") is difficult to estimate because precise dates of exposure are rarely known. Therefore, the reliability of clinical HIV testing during the first few weeks of infection is unknown, creating anxiety among HIV-exposed individuals and their physicians. We address this by fitting stochastic models of early HIV infection to detailed viral load records for 78 plasma donors, taken during the period of exposure and infection. We first show that the classic stochastic birth-death model does not satisfactorily describe early infection. We therefore apply a different stochastic model that includes infected cells and virions separately. Since every plasma donor in our data eventually becomes infected, we must condition the model to reflect this bias, before fitting to the data. Applying our best estimates of unknown parameter values, we estimate the mean eclipse period to be 8-10 days. We further estimate the reliability of a negative test t days after potential exposure.