Biology subjects

Perelson, A. S.

Publications and source records attributed to Perelson, A. S..

At least 19 recordsLinked to original sources

bioRxiv2026

A mechanistic basis for CD8+ T cell expansion sensitivity as a predictor of HIV post-treatment control

A key goal in HIV-1 cure research is to understand why some individuals control viral rebound after stopping antiretroviral therapy (ART). Recent human studies have identified responding CD8+ T cells expressing Ki-67 and the transcription factor TCF-1 as correlates of post-treatment control, but the mechanistic basis of this association remains unclear. Using the theoretical framework of Conway and Perelson, we fit mechanistic within-host models to viral load and CD8+ T cell data from 9 individuals in a combination immunotherapy trial following ART interruption. Although Ki-67 and TCF-1 measurements were not used for fitting, the inferred effector cell expansion sensitivity, i.e., the responsiveness of effector expansion to low antigen levels, shows a strong linear relationship with Ki-67 and TCF-1 levels at rebound (Pearsons r {approx} 0.8). Building on this, we show analytically that the post-rebound viral load set point is inversely proportional to the effector cell expansion sensitivity, and thus strongly correlates with cycling (Ki-67+) CD8+ T cells (r {approx} -0.8) at rebound, and a subset that expresses TCF-1 (r {approx} -0.9). In effect, individuals with a larger proportion of CD8+ T cells responding to viral rebound, and a greater representation of TCF-1 expressing cells within the responding subset, achieve markedly lower viral set points through a higher effector cell expansion sensitivity. This mechanism is consistent with prior modeling in a non-intervention ATI setting, suggesting it may generalize across more rebound contexts. Our results provide a mechanistic explanation why both Ki-67+ responding CD8+ T cells and their TCF-1-expressing subset predict post-treatment control, linking clinical correlation to its underlying cause and highlighting Ki-67 and TCF-1 as potential early biomarkers of HIV immunotherapy success.

immunology↗

bioRxiv2026

A generalized resource-allocation growth law reveals mechanisms of differential responses to intermittent androgen deprivation therapy in prostate cancer

Resource-limited growth frameworks often developed independently. Here, we derived a growth law by assuming a cell balancing internal competing resource demands and generalizing the growth signal through a shape parameter h, the trade-off exponent. In Liebig's single-limiting-resource regime, it recovers Droop's cell quota at h=1 and approaches Prater's growth efficiency as h grows large. h determines how sharply growth declines when allocation departs from optimal, thereby shaping populations evolution. Cyclic environmental stress tests such growth laws. Hence, we built a model from this growth law and applied it to prostate cancer growth in patients undergoing cyclic intermittent androgen deprivation therapy. We showed the model recapitulated longitudinal prostate specific antigen (PSA) and serum androgen measurements from 71 prostate cancer patients using a nonlinear mixed-effects framework (pooled and median individual R^2 {approx} 0.9 for PSA and {approx} 0.8 for serum androgen). h is well-constrained by the data and separates treatment outcomes (success vs. failure, p = 0.003). The results predict that failing cases have cancer that can adapt to a wide range of growth conditions with minimal cost. A simplified model recapitulated PSA dynamics from 32 prostate cancer patients on cyclic adaptive therapy (R^2 {approx} 0.8). Estimates of h for castration resistant cancer in adaptive cohort were similar to failure cases in the intermittent cohort. We found no statistical differences in estimated parameters between the adaptive vs. standard of care arms, suggesting that adaptive schedule may drive the differential outcome. The results support this growth law, providing a foundation for resource-limited growth across biological systems.

ecology↗

bioRxiv2026

Modeling quantifies in vivo neutralization, Fc-mediated killing, and resistance in human clinical trials of five anti-HIV broadly neutralizing antibodies

Broadly neutralizing antibodies (bnAbs) are a promising intervention for HIV prevention, therapy, and cure. bnAb optimization requires precise quantification of in vivo functions, many of which cannot be directly measured in humans. We therefore performed a mathematical modeling meta-analysis which integrated four clinical trials and reproduced serial bnAb concentrations, viral loads, and bnAb sensitivities (IC50) in 43 viremic trial participants who received an infusion of VRC01, VRC01LS, VRC07-523LS, 3BNC117 or 10-1074. We compared >300 mathematical models for their ability to recapitulate multi-strain HIV dynamics following bnAb infusion. For each bnAb, our best model identified a scaling factor of 36-462 to project in vivo activity from in vitro IC50, quantified Fc-mediated infected cell killing in humans over time, and projected the timing of bnAb-resistant strain emergence. Using this holistic profile, VRC07-523-LS was generally optimal.

immunology↗

bioRxiv2026

AZD5582 robustly reactivates latently infected cells and clears the majority of those reactivated from the SIV reservoir

AZD5582 (AZD) is a latency reversing agent used to support the "shock-and-kill" strategy in HIV-1 cure research. Previous studies in ART-suppressed rhesus macaques have shown that AZD can promote reactivation of latently infected cells, resulting in 2-3 log increases in on-ART viral load and significant reductions in SIV reservoir size over 5-10 doses. To quantify the impact of AZD on the reservoir, we developed an ensemble of mechanistic viral dynamic models and fit them to longitudinal plasma SIV RNA and SIV CA-DNA data from 23 macaques treated with AZD in combination with other therapies. The aggregate predictions of the model ensemble recapitulate the reactivation patterns observed in both SIV RNA and SIV CA-DNA and provide robust estimates of key parameters associated with reactivated cells. We found that AZD reactivates approximately 25% of cells in the latent reservoir per dose, with a mean reactivation duration of about 5-6 days. Of the reactivated cells, 60-79% are eventually cleared, while the remainder enter a state refractory to AZD stimulation before returning to latency. Because of this refractory state, each consecutive weekly dose reactivates about 28% fewer cells than the previous one, an effect that could be more pronounced if the refractory period substantially exceeds the interval between doses. However, the duration of this refractory state remains uncertain. Altogether, our results suggest that AZD-reactivated cells are effectively cleared. Future work should focus on improving LRAs that safely reactivate a larger fraction of the latent reservoir. Furthermore, designing experiments with varying dosing schedules can help better quantify the duration of refractoriness, which will be important for informing optimal treatment schedules and maximizing the effect of LRAs.

microbiology↗

bioRxiv2025

Modeling Phage Therapy

Patients infected with life-threatening multi-drug resistant (MDR) bacteria have been treated with cocktails of bacteriophages. This is a complicated form of personalized medicine as the phages given to a patient have to be selected beforehand on the basis of their lytic capacity of the infecting bacteria. Because bacteria rapidly become resistant, the evolution of resistance to a diverse cocktail of phages is a complicated dynamical process, during which competing bacterial strains replace one another by accumulating several resistance mechanisms, each of which may involve a fitness cost. As a consequence, it is typically not known why a particular phage therapy succeeded or failed, and how one can optimize the composition of the cocktails to maximize the rate of success. To improve upon this, we extend an existing in vivo-calibrated mouse model into a novel mathematical model for the human situation, and include multiple phages infecting multiple bacterial strains, differing in their resistance to each of the phages. We adjust several parameter estimates of the bacterial model to the human situation, and use the model to describe a successful case of phage therapy involving several cocktails, each containing several phages. In the model, treatment success crucially depended on pretreatment resistance levels, and on the diversity and the timing of the cocktails. Once an appropriate cocktail is found, it is less important to further optimize the infection rates of the phages. Resistant bacterial strains expand rapidly when sensitive strains decline, and the higher the infectivity of the phages, the faster resistant strains expand. Because resistance evolves rapidly, it is best to provide a diverse set of phages right from the start of therapy, i.e., to hit hard and early, and create a high genetic barrier to bacterial resistance.

microbiology↗

bioRxiv2024

Modeling suggests SARS-CoV-2 rebound after nirmatrelvir-ritonavir treatment is driven by target cell preservation coupled with incomplete viral clearance

In a subset of SARS-CoV-2 infected individuals treated with the oral antiviral nirmatrelvir-ritonavir, the virus rebounds following treatment. The mechanisms driving this rebound are not well understood. We used a mathematical model to describe the longitudinal viral load dynamics of 51 individuals treated with nirmatrelvir-ritonavir, 20 of whom rebounded. Target cell preservation, either by a robust innate immune response or initiation of nirmatrelvir-ritonavir near the time of symptom onset, coupled with incomplete viral clearance, appear to be the main factors leading to viral rebound. Moreover, the occurrence of viral rebound is likely influenced by time of treatment initiation relative to the progression of the infection, with earlier treatments leading to a higher chance of rebound. Finally, our model demonstrates that extending the course of nirmatrelvir-ritonavir treatment, in particular to a 10-day regimen, may greatly diminish the risk for rebound in people with mild-to-moderate COVID-19 and who are at high risk of progression to severe disease. Altogether, our results suggest that in some individuals, a standard 5-day course of nirmatrelvir-ritonavir starting around the time of symptom onset may not completely eliminate the virus. Thus, after treatment ends, the virus can rebound if an effective adaptive immune response has not fully developed. These findings on the role of target cell preservation and incomplete viral clearance also offer a possible explanation for viral rebounds following other antiviral treatments for SARS-CoV-2. ImportanceNirmatrelvir-ritonavir is an effective treatment for SARS-CoV-2. In a subset of individuals treated with nirmatrelvir-ritonavir, the initial reduction in viral load is followed by viral rebound once treatment is stopped. We show the timing of treatment initiation with nirmatrelvir-ritonavir may influence the risk of viral rebound. Nirmatrelvir-ritonavir stops viral growth and preserves target cells but may not lead to full clearance of the virus. Thus, once treatment ends, if an effective adaptive immune response has not adequately developed, the remaining virus can lead to rebound. Our results provide insights into the mechanisms of rebound and can help develop better treatment strategies to minimize this possibility.

immunology↗

bioRxiv2024

A multiscale model of the action of a capsid assembly modulator for the treatment of chronic hepatitis B

Chronic hepatitis B virus (HBV) infection is strongly associated with increased risk of liver cancer and cirrhosis. While existing treatments effectively inhibit the HBV life cycle, viral rebound occurs rapidly following treatment interruption. Consequently, functional cure rates of chronic HBV infection remain low and there is increased interest in a novel treatment modality, capsid assembly modulators (CAMs). Here, we develop a multiscale mathematical model of CAM treatment in chronic HBV infection. By fitting the model to participant data from a phase I trial of the first-generation CAM vebicorvir, we estimate the drugs dose-dependent effectiveness and identify the physiological mechanisms that drive the observed biphasic decline in HBV DNA and RNA, and mechanistic differences between HBeAg-positive and negative infection. Finally, we demonstrate analytically and numerically that HBV RNA is more sensitive than HBV DNA to increases in CAM effectiveness. Author summaryCapsid assembly modulators (CAMs) are a novel class of anti-hepatitis B virus (HBV) treatments in clinical trials. These CAMs have a distinct mechanism of action from nucleos(t)ide analogues and thus represent an attractive option for the treatment of chronic HBV infection. We developed a multiscale model of the intracellular HBV lifecycle and extracellular dynamics using a time-since-infection structured partial differential equation. We fit the model to participant data from a recent phase I trial, performed a detailed parameter sensitivity analysis, identified key mechanisms driving viral response to first-generation CAM treatment, and demonstrated that HBV RNA is more sensitive than HBV DNA to changes in CAM efficacy, highlighting the potential role of HBV RNA as a biomarker for CAM effectiveness.

microbiology↗

bioRxiv2024

Quantification of heterogeneity in human CD8+ T cell responses to vaccine antigens: an HLA-guided perspective

Vaccines have historically played a pivotal role in controlling epidemics. Effective vaccines for viruses causing significant human disease, e.g., Ebola, Lassa fever, or Crimean Congo hemorrhagic fever virus, would be invaluable to public health strategies and counter-measure development missions. Here, we propose coverage metrics to quantify vaccine-induced CD8+T cell-mediated immune protection, as well as metrics to characterize immuno-dominant epitopes, in light of human genetic heterogeneity and viral evolution. Proof-of-principle of our approach and methods will be demonstrated for Ebola virus, SARS-CoV-2, and Burkholderia pseudomallei (vaccine) proteins.

immunology↗

bioRxiv2024

Understanding early HIV-1 rebound dynamics following antiretroviral therapy interruption: The importance of effector cell expansion

Most people living with HIV-1 experience rapid viral rebound once antiretroviral therapy is interrupted; however, a small fraction remain in viral remission for an extended duration. Understanding the factors that determine whether viral rebound is likely after treatment interruption can enable the development of optimal treatment regimens and therapeutic interventions to potentially achieve a functional cure for HIV-1. We built upon the theoretical framework proposed by Conway and Perelson to construct dynamic models of virus-immune interactions to study factors that influence viral rebound dynamics. We evaluated these models using viral load data from 24 individuals following antiretroviral therapy interruption. The best-performing model accurately captures the heterogeneity of viral dynamics and highlights the importance of the effector cell expansion rate. Our results show that post-treatment controllers and non-controllers can be distinguished based on the effector cell expansion rate in our models. Furthermore, these results demonstrate the potential of using dynamic models incorporating an effector cell response to understand early viral rebound dynamics post-antiretroviral therapy interruption.

immunology↗

bioRxiv2023

Last in first out: SIV proviruses seeded later in infection are harbored in short-lived CD4+ T cells

HIV can persist in a latent form as integrated DNA (provirus) in resting CD4+ T cells of infected individuals and as such is unaffected by antiretroviral therapy (ART). Despite being a major obstacle for eradication efforts, the genetic variation and timing of formation of this latent reservoir remains poorly understood. Previous studies on when virus is deposited in the latent reservoir have come to contradictory conclusions. To reexamine the genetic variation of HIV in CD4+ T cells during ART, we determined the divergence in envelope sequences collected from 10 SIV infected rhesus macaques. We found that the macaques displayed a biphasic decline of the viral divergence over time, where the first phase lasted for an average of 11.6 weeks (range 4-28 weeks). Motivated by recent observations that the HIV-infected CD4+ T cell population is composed of short- and long-lived subsets, we developed a model to study the divergence dynamics. We found that SIV in short-lived cells was on average more diverged, while long-lived cells harbored less diverged virus. This suggests that the long-lived cells harbor virus deposited starting earlier in infection and continuing throughout infection, while short-lived cells predominantly harbor more recent virus. As these cell populations decayed, the overall proviral divergence decline matched that observed in the empirical data. This model explains previous seemingly contradictory results on the timing of virus deposition into the latent reservoir, and should provide guidance for future eradication efforts. Significance statementHIV can persist in a latent reservoir unaffected by antiretroviral drugs. The genetic variation of this latent virus population is a major obstacle for eradication efforts, but also a clue to when HIV variants are deposited in the reservoirs. Unfortunately, previous studies assessing when the virus was deposited in latent reservoirs have come to contradictory conclusions. Here, we propose SIV proviral DNA exists in both short- and long-lived CD4+ T cells, and that these two cell subsets harbor different genetically diverged virus populations. Our model explains the contradictory findings and shows that when CD4+ T cells decay under effective drug treatment, which prevents virus replication, the resulting virus divergence decreases and recapitulates observed data. This knowledge should help in improving future eradication efforts.

microbiology↗

bioRxiv2023

Modeling the emergence of viral resistance for SARS-CoV-2 during treatment with an anti-spike monoclonal antibody

The COVID-19 pandemic has led to over 760 million cases and 6.9 million deaths worldwide. To mitigate the loss of lives, emergency use authorization was given to several anti-SARS-CoV-2 monoclonal antibody (mAb) therapies for the treatment of mild-to-moderate COVID-19 in patients with a high risk of progressing to severe disease. Monoclonal antibodies used to treat SARS-CoV-2 target the spike protein of the virus and block its ability to enter and infect target cells. Monoclonal antibody therapy can thus accelerate the decline in viral load and lower hospitalization rates among high-risk patients with susceptible variants. However, viral resistance has been observed, in some cases leading to a transient viral rebound that can be as large as 3-4 orders of magnitude. As mAbs represent a proven treatment choice for SARS-CoV-2 and other viral infections, evaluation of treatment-emergent mAb resistance can help uncover underlying pathobiology of SARS-CoV-2 infection and may also help in the development of the next generation of mAb therapies. Although resistance can be expected, the large rebounds observed are much more difficult to explain. We hypothesize replenishment of target cells is necessary to generate the high transient viral rebound. Thus, we formulated two models with different mechanisms for target cell replenishment (homeostatic proliferation and return from an innate immune response anti-viral state) and fit them to data from persons with SARS-CoV-2 treated with a mAb. We showed that both models can explain the emergence of resistant virus associated with high transient viral rebounds. We found that variations in the target cell supply rate and adaptive immunity parameters have a strong impact on the magnitude or observability of the viral rebound associated with the emergence of resistant virus. Both variations in target cell supply rate and adaptive immunity parameters may explain why only some individuals develop observable transient resistant viral rebound. Our study highlights the conditions that can lead to resistance and subsequent viral rebound in mAb treatments during acute infection. Author summaryMonoclonal antibodies have been used as a treatment for SARS-CoV-2. However, viral evolution and development of variants has compromised the use of all currently authorized monoclonal antibodies for SARS-CoV-2. In some individuals treated with one such monoclonal antibody, bamlanivimab, transient nasal viral rebounds of 3-4 logs associated with resistant viral strains occur. To better understand the mechanisms underlying resistance emergence with high viral load rebounds, we developed two different models that incorporate drug sensitive and drug resistant virus as well as target cell replenishment and fit them to data. The models accurately capture the observed viral dynamics as well as the proportion of resistant virus for each studied individual with little variation in model parameters. In the models with best-fit parameters, bamlanivimab selects for resistance mutants that can expand to high levels due to target cell replenishment. The ultimate clearance of virus however depends on the development of adaptive immunity.

immunology↗

bioRxiv2023

How reliable are estimates of key parameters in viral dynamic models?

Mathematical models of viral infection have been developed and fit to data to gain insight into disease pathogenesis for a number of agents including HIV, hepatitis C and B virus. However, for acute infections such as influenza and SARS-CoV-2, as well as for infections such as hepatitis C and B that can be acute or progress to being chronic, viral load data are often collected after symptoms develop, usually around or after the peak viral load. Consequently, we frequently lack data in the exponential phase of viral growth, i.e., when most transmission events occur. Missing data may make estimation of the time of infection, the infectious period, and parameters in viral dynamic models, such as the cell infection rate, difficult. Here, we evaluated the reliability of estimates of key model parameters when viral load data prior to the viral load peak is missing. We estimated the time from infection to peak viral load by fitting non-linear mixed models to a dataset with frequent viral RNA measurements, including pre-peak. We quantified the reliability of estimated infection times, key model parameters, and the time to peak viral load. Although estimates of the time of infection are sensitive to the quality and amount of available data, other parameters important in understanding disease pathogenesis, such as the loss rate of infected cells, are less sensitive. We find a lack of data in the exponential growth phase underestimates the time to peak viral load by several days leading to a shorter predicted exponential growth phase. On the other hand, having an idea of the time of infection and fixing it, results in relatively good estimates of dynamical parameters even in the absence of early data.

immunology↗

bioRxiv2023

Multiscale modeling of HBV infection integrating intra- and intercellular viral propagation for analyzing extracellular viral markers

Chronic infection of hepatitis B virus (HBV) is caused by the persistence of closed circular DNA (cccDNA) in the nucleus of infected hepatocytes. Despite available therapeutic anti-HBV agents, eliminating the cccDNA remains challenging. The quantifying and understanding dynamics of cccDNA are essential for developing effective treatment strategies and new drugs. However, it requires a liver biopsy to measure the intrahepatic cccDNA, which is basically not accepted because of the ethical aspect. We here aimed to develop a non-invasive method for quantifying cccDNA in the liver using surrogate markers present in peripheral blood. We constructed a multiscale mathematical model that explicitly incorporates both intracellular and intercellular HBV infection processes. The model, based on age-structured partial differential equations (PDEs), integrates experimental data from in vitro and in vivo investigations. By applying this model, we successfully predicted the amount and dynamics of intrahepatic cccDNA using specific viral markers in serum samples, including HBV DNA, HBsAg, HBeAg, and HBcrAg. Our study represents a significant step towards advancing the understanding of chronic HBV infection. The non-invasive quantification of cccDNA using our proposed methodology holds promise for improving clinical analyses and treatment strategies. By comprehensively describing the interactions of all components involved in HBV infection, our multiscale mathematical model provides a valuable framework for further research and the development of targeted interventions.

microbiology↗

bioRxiv2022

Prediction of elimination of intrahepatic cccDNA in hepatitis B virus-infected patients by a combination of noninvasive viral markers

Evaluation of intrahepatic covalently closed circular DNA (cccDNA) is a key for searching an elimination of hepatitis B virus (HBV) infection. HBV RNA and HBV core-related antigen have been proposed as surrogate markers for evaluating cccDNA activity, although they do not necessarily estimate the amount of cccDNA. Here, we developed a novel multiscale mathematical model describing intra- and inter-cellular viral propagation, based on the experimental quantification data in both HBV-infected cell culture and humanized mouse models. We applied it to HBV-infected patients under treatment and developed a model which can predict intracellular HBV dynamics only by use of noninvasive extracellular surrogate biomarkers. Importantly, the model prediction of the amount of cccDNA in patients over time was confirmed to be well-correlated with the liver biopsy data. Thus, our noninvasive method enables to predict the amount of cccDNA in patients and contributes to determining the treatment endpoint required for elimination of intrahepatic cccDNA.

microbiology↗

bioRxiv2022

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

Plus-strand RNA viruses are the largest group of viruses. Many are human pathogens that inflict a socio-economic burden. Interestingly, plus-strand RNA viruses share remarkable similarities in their replication. A hallmark of plus-strand RNA viruses is the remodeling of intracellular membranes to establish replication organelles (so-called "replication factories"), which provide a protected environment for the replicase complex, consisting of the viral genome and proteins necessary for viral RNA synthesis. In the current study, we investigate pan-viral similarities and virus-specific differences in the life cycle of this highly relevant group of viruses. We first measured the kinetics of viral RNA, viral protein, and infectious virus particle production of hepatitis C virus (HCV), dengue virus (DENV), and coxsackievirus B3 (CVB3) in the immuno-compromised Huh7 cell line and thus without perturbations by an intrinsic immune response. Based on these measurements, we developed a detailed mathematical model of the replication of HCV, DENV, and CVB3 and show that only small virus-specific changes in the model were necessary to describe the in vitro dynamics of the different viruses. Our model correctly predicted virus-specific mechanisms such as host cell translation shut off and different kinetics of replication organelles. Further, our model suggests that the ability to suppress or shut down host cell mRNA translation may be a key factor for in vitro replication efficiency which may determine acute self-limited or chronic infection. We further analyzed potential broad-spectrum antiviral treatment options in silico and found that targeting viral RNA translation, especially polyprotein cleavage, and viral RNA synthesis may be the most promising drug targets for all plus-strand RNA viruses. Moreover, we found that targeting only the formation of replicase complexes did not stop the viral replication in vitro early in infection, while inhibiting intracellular trafficking processes may even lead to amplified viral growth. Author summaryPlus-strand RNA viruses comprise a large group of related and medically relevant viruses. The current global pandemic of COVID-19 caused by the SARS-coronavirus-2 as well as the constant spread of diseases such as dengue and chikungunya fever show the necessity of a comprehensive and precise analysis of plus-strand RNA virus infections. Plus-strand RNA viruses share similarities in their life cycle. To understand their within-host replication strategies, we developed a mathematical model that studies pan-viral similarities and virus-specific differences of three plus-strand RNA viruses, namely hepatitis C, dengue, and coxsackievirus. By fitting our model to in vitro data, we found that only small virus-specific variations in the model were required to describe the dynamics of all three viruses. Furthermore, our model predicted that ribosomes involved in viral RNA translation seem to be a key player in plus-strand RNA replication efficiency, which may determine acute or chronic infection outcome. Furthermore, our in-silico drug treatment analysis suggests that targeting viral proteases involved in polyprotein cleavage, in combination with viral RNA replication, may represent promising drug targets with broad-spectrum antiviral activity.

microbiology↗

bioRxiv2020

Models of SIV rebound after treatment interruption that involve multiple reactivation events

In order to assess the efficacy of novel HIV-1 treatments leading to a functional cure, the time to viral rebound is frequently used as a surrogate endpoint. The longer the time to viral rebound, the more efficacious the therapy. In support of such an approach, mathematical models serve as a connection between the size of the latent reservoir and the time to HIV-1 rebound after treatment interruption. The simplest of such models assumes that a single successful latent cell reactivation event leads to observable viremia after a period of exponential viral growth. Here we consider a generalization developed by Pinkevych et al. and Hill et al. of this simple model in which multiple reactivation events can occur, each contributing to the exponential growth of the viral load. We formalize and improve the previous derivation of the dynamics predicted by this model, and use the model to estimate relevant biological parameters from SIV rebound data. We confirm a previously described effect of very early antiretroviral therapy (ART) initiation on the rate of recrudescence and the viral load growth rate after treatment interruption. We find that every day ART initiation is delayed results in a 39% increase in the recrudescence rate, and a 11% decrease of the viral growth rate. We show that when viral rebound occurs early relative to the viral load doubling time, a model with multiple successful reactivation events fits the data better than a model with only a single successful reactivation event. Author SummaryHIV-1 persists during suppressive antiretroviral therapy (ART) due to a reservoir of latently infected cells. When ART is stopped, HIV generally rebounds within a few weeks. However, there is a small fraction of patients that do not rebound over a period of months or years. A variety of treatments are being tested for their ability to reduce the size of the latent reservoir, to induce effective immune responses against the virus, or to prevent or prolong the time to viral rebound after ART interruption. These novel treatments are typically first tested in SIV infected macaques, and the efficacy of the treatment assessed by interrupting ART and measuring the time to viral rebound. Here, we develop and test a mathematical and statistical model that describes the process of viral rebound. The model can be used for statistical inference of the efficacy of newly developed treatments. Importantly, the model takes into account that multiple recrudescence events can precede rebound. We test the model using data from early treated SIV infected macaques.

immunology↗

bioRxiv2020

Understanding hepatitis B virus dynamics and the antiviral effect of interferon-alpha treatment in humanized chimeric mice

BackgroundWhereas the mode of action of lamivudine (LAM) against hepatitis B virus (HBV) is well established, the inhibition mechanism(s) of interferon- are less completely defined. To advance our understanding, we mathematically modelled HBV kinetics during pegylated interferon--2a (pegIFN), LAM and pegIFN+LAM treatment of chronically HBV-infected humanized uPA/SCID chimeric mice. MethodsThirty-nine uPA/SCID mice with humanized livers whose pre-treatment steady-state serum HBV reached 9.2{+/-}0.4 logIU/mL were treated with pegIFN, LAM or pegIFN+LAM for 14 days. Serum HBV DNA and intracellular HBV DNA were measured frequently. We developed a nonlinear mixed effect viral kinetic model and simultaneously fit it to the serum and intracellular HBV DNA data. ResultsUnexpectedly, even in the absence of an adaptive-immune response, a biphasic decline in serum HBV DNA and intracellular HBV DNA was observed in response to all treatments. Modeling predicts that the first phase represents pegIFN inhibiting intracellular HBV DNA synthesis with efficacy of 86%, which was similar under LAM and pegIFN+LAM. In contrast, there were distinct differences in HBV decline during the 2nd phase which was accounted for in the model by a time-dependent inhibition of intracellular HBV DNA synthesis with the steepest decline observed during pegIFN+LAM (0.46/d) and the slowest (0.052/d) during pegIFN mono-treatment. ConclusionsReminiscent of observations in patients treated with pegIFN and/or LAM, a biphasic HBV decline was observed in treated humanized mice in the absence of adaptive immune response. Interestingly, combination treatment does not increase the initial inhibition of HBV production; however, enhancement of second phase decline is observed providing insight into the dynamics of HBV treatment response and the mode of action of interferon- against HBV. Author SummaryChronic hepatitis B virus (HBV) infection remains a global health care problem as we lack sufficient curative treatment options. Elucidating the dynamic of HBV infection and treatment at the molecular level would potentially facilitate the development of novel, more effective HBV antivirals. Currently, the only well-established small animal HBV infection model available is the chimeric uPA/SCID mice with humanized livers; however, the HBV infection kinetics under interferon- (IFN) in this model system have not been determined in sufficient detail to support the in-depth studies of HBV treatment response needed to identify/confirm more effective drug targets. In this study 39 chronic HBV-infected uPA/SCID humanized mice treated with IFN and/or lamivudine were analysed using a mathematical modelling approach. We found that IFN main mode of action is blocking HBV DNA synthesis and that 73% of synthesized HBV DNA per are secreted from infected cells. Our data-driven mathematical modeling study provides novel insights into IFN anti-HBV mechanism(s) and viral-host interplay at the molecular level.

systems biology↗

bioRxiv2018

Regulation of T cell expansion by antigen presentation dynamics

An essential feature of the adaptive immune system is the proliferation of antigen-specific lymphocytes during an immune reaction to form a large pool of effector cells. This proliferation must be regulated to ensure an effective response to infection while avoiding immunopathology. Recent experiments in mice have demonstrated that the expansion of a specific clone of T cells in response to cognate antigen obeys a striking inverse power law with respect to the initial number of T cells. Here, we show that such a relationship arises naturally from a model in which T cell expansion is limited by decaying levels of presented antigen. The same model also accounts for the observed dependence of T cell expansion on affinity for antigen and on the kinetics of antigen administration. Extending the model to address expansion of multiple T cell clones competing for antigen, we find that higher affinity clones can suppress the proliferation of lower affinity clones, thereby promoting the specificity of the response. Employing the model to derive optimal vaccination protocols, we find that exponentially increasing antigen doses can achieve a nearly optimized response. We thus conclude that the dynamics of presented antigen is a key regulator of both the size and specificity of the adaptive immune response.

immunology↗