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1.  Quantifying the Turnover of Transcriptional Subclasses of HIV-1-Infected Cells 
PLoS Computational Biology  2014;10(10):e1003871.
HIV-1-infected cells in peripheral blood can be grouped into different transcriptional subclasses. Quantifying the turnover of these cellular subclasses can provide important insights into the viral life cycle and the generation and maintenance of latently infected cells. We used previously published data from five patients chronically infected with HIV-1 that initiated combination antiretroviral therapy (cART). Patient-matched PCR for unspliced and multiply spliced viral RNAs combined with limiting dilution analysis provided measurements of transcriptional profiles at the single cell level. Furthermore, measurement of intracellular transcripts and extracellular virion-enclosed HIV-1 RNA allowed us to distinguish productive from non-productive cells. We developed a mathematical model describing the dynamics of plasma virus and the transcriptional subclasses of HIV-1-infected cells. Fitting the model to the data allowed us to better understand the phenotype of different transcriptional subclasses and their contribution to the overall turnover of HIV-1 before and during cART. The average number of virus-producing cells in peripheral blood is small during chronic infection. We find that a substantial fraction of cells can become defectively infected. Assuming that the infection is homogenous throughout the body, we estimate an average in vivo viral burst size on the order of 104 virions per cell. Our study provides novel quantitative insights into the turnover and development of different subclasses of HIV-1-infected cells, and indicates that cells containing solely unspliced viral RNA are a good marker for viral latency. The model illustrates how the pool of latently infected cells becomes rapidly established during the first months of acute infection and continues to increase slowly during the first years of chronic infection. Having a detailed understanding of this process will be useful for the evaluation of viral eradication strategies that aim to deplete the latent reservoir of HIV-1.
Author Summary
Gaining a quantitative understanding of the development and turnover of different HIV-1-infected subpopulations of cells is crucial to improve the outcome of patients on combination antiretroviral therapy (cART). The population of latently infected cells is of particular interest as they represent the major barrier to a cure of HIV-1 infection. We developed a mathematical model that describes the dynamics of different transcriptionally active subclasses of HIV-1-infected cells and the viral load in peripheral blood. The model was fitted to previously published data from five chronically HIV-1-infected patients starting cART. This allowed us to estimate critical parameters of the within-host dynamics of HIV-1, such as the the number of virions produced by a single infected cell. The model further allowed investigation of HIV-1 dynamics during the acute phase. Computer simulations illustrate that latently infected cells become rapidly established during the first months of acute infection and continue to increase slowly during the first years of chronic infection. This illustrates the opportunity for strategies that aim to eradicate the virus during early cART as the pool of HIV-1 infected cells is substantially smaller during acute infection than during chronic infection.
PMCID: PMC4207463  PMID: 25340797
2.  Antigen Load and Viral Sequence Diversification Determine the Functional Profile of HIV-1–Specific CD8+ T Cells 
PLoS Medicine  2008;5(5):e100.
Virus-specific CD8+ T lymphocytes play a key role in the initial reduction of peak viremia during acute viral infections, but display signs of increasing dysfunction and exhaustion under conditions of chronic antigen persistence. It has been suggested that virus-specific CD8+ T cells with a “polyfunctional” profile, defined by the capacity to secrete multiple cytokines or chemokines, are most competent in controlling viral replication in chronic HIV-1 infection. We used HIV-1 infection as a model of chronic persistent viral infection to investigate the process of exhaustion and dysfunction of virus-specific CD8+ T cell responses on the single-epitope level over time, starting in primary HIV-1 infection.
Methods and Findings
We longitudinally analyzed the polyfunctional epitope-specific CD8+ T cell responses of 18 patients during primary HIV-1 infection before and after therapy initiation or sequence variation in the targeted epitope. Epitope-specific CD8+ T cells responded with multiple effector functions to antigenic stimulation during primary HIV-1 infection, but lost their polyfunctional capacity in response to antigen and up-regulated programmed death 1 (PD-1) expression with persistent viremic infection. This exhausted phenotype significantly decreased upon removal of stimulation by antigen, either in response to antiretroviral therapy or by reduction of epitope-specific antigen load in the presence of ongoing viral replication, as a consequence of in vivo selection of cytotoxic T lymphocyte escape mutations in the respective epitopes. Monofunctionality increased in CD8+ T cell responses directed against conserved epitopes from 49% (95% confidence interval 27%–72%) to 76% (56%–95%) (standard deviation [SD] of the effect size 0.71), while monofunctionality remained stable or slightly decreased for responses directed against escaped epitopes from 61% (47%–75%) to 56% (42%–70%) (SD of the effect size 0.18) (p < 0.05).
These data suggest that persistence of antigen can be the cause, rather than the consequence, of the functional impairment of virus-specific T cell responses observed during chronic HIV-1 infection, and underscore the importance of evaluating autologous viral sequences in studies aimed at investigating the relationship between virus-specific immunity and associated pathogenesis.
Marcus Altfeld and colleagues suggest that the exhaustion of virus-specific CD8+ T cells during chronic HIV infection likely results from the persistence of antigen.
Editors' Summary
Viruses are small infectious agents responsible for many human diseases, including acquired immunodeficiency syndrome (AIDS). Like other viruses, the human immunodeficiency virus 1 (HIV-1; the cause of AIDS) enters human cells and uses the cellular machinery to replicate before bursting out of its temporary home. During the initial stage of HIV infection, a particular group of cells in the human immune system, CD8+ T cells, are thought to be important in controlling the level of the virus. These immune system cells recognize pieces of viral protein called antigens displayed on the surface of infected cells; different subsets of CD8+ T cells recognize different antigens. When a CD8+ T cell recognizes its specific antigen (or more accurately, a small part of the antigen called an “epitope”), it releases cytotoxins (which kill the infected cells) and cytokines, proteins that stimulate CD8+ T cell proliferation and activate other parts of the immune system. With many viruses, when a person first becomes infected (an acute viral infection), antigen-specific CD8+ T cells completely clear the infection. But with HIV-1 and some other viruses, these cells do not manage to remove all the viruses from the body and a chronic (long-term) infection develops, during which the immune system is constantly exposed to viral antigen.
Why Was This Study Done?
In HIV-1 infections (and other chronic viral infections), virus-specific CD8+ T cells lose their ability to proliferate, to make cytokines, and to kill infected cells as patients progress to the long-term stages of infection. That is, the virus-specific CD8+ T cells gradually lose their “effector” functions and become functionally impaired or “exhausted.” “Polyfunctional” CD8+ T cells (those that release multiple cytokines in response to antigen) are believed to be essential for an effective CD8+ T cell response, so scientists trying to develop HIV-1 vaccines would like to stimulate the production of this type of cell. To do this they need to understand why these polyfunctional cells are lost during chronic infections. Is their loss the cause or the result of viral persistence? In other words, does the constant presence of viral antigen lead to the exhaustion of CD8+ T cells during chronic HIV infection? In this study, the researchers investigate this question by looking at the polyfunctionality of CD8+ cells responding to several different viral epitopes at various times during HIV-1 infection, starting very early after infection with HIV-1 had occurred.
What Did the Researchers Do and Find?
The researchers enrolled 18 patients recently infected with HIV-1 and analyzed their CD8+ T cell responses to specific epitopes at various times after enrollment using a technique called flow cytometry. They found that the epitope-specific CD8+ cells produced several effector proteins after antigen stimulation during the initial stage of HIV-1 infection, but lost their polyfunctionality in the face of persistent viral infection. The CD8+ T cells also increased their production of programmed death 1 (PD-1), a protein that has been shown to be associated with the functional impairment of CD8+ T cells. Some of the patients began antiretroviral therapy during the study, and the researchers found that this treatment, which reduced the viral load, reversed CD8+ T cell exhaustion. Finally, the appearance in the patients' blood of viruses that had made changes in the specific epitopes recognized by the CD8+ T cells to avoid being killed by these cells, also reversed the exhaustion of the T cells recognizing these particular epitopes.
What Do These Findings Mean?
These findings suggest that the constant presence of HIV-1 antigen causes the functional impairment of virus-specific CD8+ T cell responses during chronic HIV-1 infections. Treatment with antiretroviral drugs reversed this functional impairment by reducing the amount of antigen in the patients. Similarly, the appearance of viruses with altered epitopes, which effectively reduced the amount of antigen recognized by those epitope-specific CD8+ T cells without reducing the viral load, also reversed T cell exhaustion. These results would not have been seen if the functional impairment of CD8+ cells were the cause rather than the result of antigen persistence. By providing new insights into how the T cell response to viruses evolves during persistent viral infections, these findings should help in the design of vaccines against HIV and other viruses that cause chronic viral infections.
Additional Information.
Please access these Web sites via the online version of this summary at
Read a related PLoS Medicine Research in Translation article
Learn more from the researchers' Web site, the Partners AIDS Research Center
Wikipedia has a page on cytotoxic T cells (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
Information is available from the US National Institute of Allergy and Infectious Diseases on HIV infection and AIDS
HIV InSite has comprehensive information on all aspects of HIV/AIDS, including a detailed article on the immunopathogenesis of HIV infection
NAM, a UK registered charity, provides information about all aspects of HIV and AIDS, including a fact sheet on the stages of HIV infection and on the immune response to HIV
Information is available from Avert, an international AIDS charity, on all aspects of HIV/AIDS, including information on the stages of HIV infection
PMCID: PMC2365971  PMID: 18462013
3.  Effect of Synaptic Transmission on Viral Fitness in HIV Infection 
PLoS ONE  2012;7(11):e48361.
HIV can spread through its target cell population either via cell-free transmission, or by cell-to-cell transmission, presumably through virological synapses. Synaptic transmission entails the transfer of tens to hundreds of viruses per synapse, a fraction of which successfully integrate into the target cell genome. It is currently not understood how synaptic transmission affects viral fitness. Using a mathematical model, we investigate how different synaptic transmission strategies, defined by the number of viruses passed per synapse, influence the basic reproductive ratio of the virus, R0, and virus load. In the most basic scenario, the model suggests that R0 is maximized if a single virus particle is transferred per synapse. R0 decreases and the infection eventually cannot be maintained for larger numbers of transferred viruses, because multiple infection of the same cell wastes viruses that could otherwise enter uninfected cells. To explain the relatively large number of HIV copies transferred per synapse, we consider additional biological assumptions under which an intermediate number of viruses transferred per synapse could maximize R0. These include an increased burst size in multiply infected cells, the saturation of anti-viral factors upon infection of cells, and rate limiting steps during the process of synapse formation.
PMCID: PMC3499495  PMID: 23166585
4.  HIV-1 Transmission during Early Infection in Men Who Have Sex with Men: A Phylodynamic Analysis 
PLoS Medicine  2013;10(12):e1001568.
Erik Volz and colleagues use HIV genetic information from a cohort of men who have sex with men in Detroit, USA to dissect the timing of onward transmission during HIV infection.
Please see later in the article for the Editors' Summary
Conventional epidemiological surveillance of infectious diseases is focused on characterization of incident infections and estimation of the number of prevalent infections. Advances in methods for the analysis of the population-level genetic variation of viruses can potentially provide information about donors, not just recipients, of infection. Genetic sequences from many viruses are increasingly abundant, especially HIV, which is routinely sequenced for surveillance of drug resistance mutations. We conducted a phylodynamic analysis of HIV genetic sequence data and surveillance data from a US population of men who have sex with men (MSM) and estimated incidence and transmission rates by stage of infection.
Methods and Findings
We analyzed 662 HIV-1 subtype B sequences collected between October 14, 2004, and February 24, 2012, from MSM in the Detroit metropolitan area, Michigan. These sequences were cross-referenced with a database of 30,200 patients diagnosed with HIV infection in the state of Michigan, which includes clinical information that is informative about the recency of infection at the time of diagnosis. These data were analyzed using recently developed population genetic methods that have enabled the estimation of transmission rates from the population-level genetic diversity of the virus. We found that genetic data are highly informative about HIV donors in ways that standard surveillance data are not. Genetic data are especially informative about the stage of infection of donors at the point of transmission. We estimate that 44.7% (95% CI, 42.2%–46.4%) of transmissions occur during the first year of infection.
In this study, almost half of transmissions occurred within the first year of HIV infection in MSM. Our conclusions may be sensitive to un-modeled intra-host evolutionary dynamics, un-modeled sexual risk behavior, and uncertainty in the stage of infected hosts at the time of sampling. The intensity of transmission during early infection may have significance for public health interventions based on early treatment of newly diagnosed individuals.
Please see later in the article for the Editors' Summary
Editors' Summary
Since the first recorded case of AIDS in 1981, the number of people infected with HIV, the virus that causes AIDS, has risen steadily. About 34 million people are currently HIV-positive, and about 2.5 million people become newly infected with HIV every year. Because HIV is usually transmitted through unprotected sex with an infected partner, individuals can reduce their risk of infection by abstaining from sex, by having only one or a few partners, and by always using condoms. Most people do not become ill immediately after infection with HIV, although some develop a short flu-like illness. The next stage of HIV infection, which may last more than ten years, also has no major symptoms, but during this stage, HIV slowly destroys immune system cells. Eventually, the immune system can no longer fight off infections by other disease-causing organisms, and HIV-positive people then develop one or more life-threatening AIDS-defining conditions, including unusual infections and specific types of cancer. HIV infection can be controlled, but not cured, by taking a daily cocktail of antiretroviral drugs.
Why Was This Study Done?
The design of effective programs to prevent the spread of HIV/AIDS depends on knowing how HIV transmissibility varies over the course of HIV infection. Consider, for example, a prevention strategy that focuses on increasing treatment rates: antiretroviral drugs, in addition to reducing illness and death among HIV-positive people, reduce HIV transmission from HIV-positive individuals. “Treatment as prevention” can only block transmissions that occur after diagnosis and entry into care. However, the transmissibility of HIV per sexual contact depends on a person's viral load, which peaks during early HIV infection, when people are often unaware of their HIV status and may still be following the high-risk patterns of sexual behavior that caused their own infection. Epidemiological surveillance data (information on HIV infections within populations) can be used to estimate how many new HIV infections occur within a population annually (HIV incidence) and the proportion of the population that is HIV-positive (HIV prevalence), but cannot be used to estimate the timing of transmission events. In this study, the researchers use “phylodynamic analysis” to estimate HIV incidence and prevalence and the timing of HIV transmission during infection. HIV, like many other viruses, rapidly accumulates genetic changes. The timing of transmission influences the pattern of these changes. Viral phylodynamic analysis—the quantitative study of how epidemiological, immunological, and evolutionary processes shape viral phylogenies (evolutionary trees)—can therefore provide estimates of transmission dynamics.
What Did the Researchers Do and Find?
The researchers obtained HIV sequence data (collected for routine surveillance of antiretroviral resistance mutations) and epidemiological surveillance data (including information on the stage of infection at diagnosis) for 662 HIV-positive men who have sex with men living in the Detroit metropolitan area of Michigan. They constructed a phylogenetic tree from the sequences using a “relaxed clock” approach and then fitted an epidemiological model (a mathematical model that represents the progress of individual patients through various stages of HIV infection) to the sequence data. Their approach, which integrates surveillance data and genetic data, yielded estimates of HIV incidence and prevalence among the study population similar to those obtained from surveillance data alone. However, it also provided information about HIV transmission that could not be obtained from surveillance data alone. In particular, it allowed the researchers to estimate that, in the current HIV epidemic among men who have sex with men in Detroit, 44.7% of HIV transmissions occur during the first year of infection.
What Do These Findings Mean?
The robustness of these findings depends on the validity of the assumptions included in the researchers' population genetic model and on the accuracy of the data fed into the model, and may not be generalizable to other cities or to other risk groups. Nevertheless, the findings of this analysis, which can be repeated in any setting where HIV sequence data for individual patients can be linked to patient-specific clinical and behavioral information, have important implications for HIV control strategies based on the early treatment of newly diagnosed individuals. Because relatively few infected individuals are diagnosed during early HIV infection, when the HIV transmission rate is high, it is unlikely, suggest the researchers, that the “treatment as prevention” strategy will effectively control the spread of HIV unless there are very high rates of HIV testing and treatment.
Additional Information
Please access these websites via the online version of this summary at
This study is further discussed in a PLOS Medicine Perspective by Timothy Hallett
Information is available from the US National Institute of Allergy and Infectious Diseases on HIV infection and AIDS
NAM/aidsmap provides basic information about HIV/AIDS and summaries of recent research findings on HIV care and treatment
Information is available from Avert, an international AIDS charity, on many aspects of HIV/AIDS, including information on HIV treatment as prevention (in English and Spanish)
The PLOS Medicine Collection Investigating the Impact of Treatment on New HIV Infections provides more information about HIV treatment as prevention
A PLOS Computational Biology Topic Page (a review article that is a published copy of record of a dynamic version of the article as found in Wikipedia) about viral phylodynamics is available
The US National Institute of Health–funded HIV Sequence Database contains HIV sequences and tools to analyze these sequences
Patient stories about living with HIV/AIDS are available through Avert; the charity website Healthtalkonline also provides personal stories about living with HIV
PMCID: PMC3858227  PMID: 24339751
5.  Frequent Coinfection of Cells Explains Functional In Vivo Complementation between Cytomegalovirus Variants in the Multiply Infected Host 
Journal of Virology  2005;79(15):9492-9502.
In contrast to many other virus infections, primary cytomegalovirus (CMV) infection does not fully protect against reinfection. Accordingly, clinical data have revealed a coexistence of multiple human CMV variants/strains in individual patients. Notably, the phenomenon of multiple infection was found to correlate with increased virus load and severity of CMV disease. Although of obvious medical relevance, the mechanism underlying this correlation is unknown. A weak immune response in an individual could be responsible for a more severe disease and for multiple infections. Alternatively, synergistic contributions of variants that differ in their biological properties can lead to qualitative changes in viral fitness by direct interactions such as genetic recombination or functional complementation within coinfected host cells. We have addressed this important question paradigmatically with the murine model by differently designed combinations of two viruses employed for experimental coinfection of mice. Specifically, a murine cytomegalovirus (MCMV) mutant expressing Cre recombinase was combined for coinfection with a mutant carrying Cre-inducible green fluorescent protein gene, and attenuated mutants were combined for coinfection with wild-type virus followed by two-color in situ hybridization studies visualizing the replication of the two viruses in infected host organs. These different approaches concurred in the conclusion that coinfection of host cells is more frequent than statistically predicted and that this coinfection alters virus fitness by functional trans-complementation rather than by genetic recombination. The reported findings make a major contribution to our molecular understanding of enhanced CMV pathogenicity in the multiply infected host.
PMCID: PMC1181553  PMID: 16014912
6.  Reassessment of HIV-1 Acute Phase Infectivity: Accounting for Heterogeneity and Study Design with Simulated Cohorts 
PLoS Medicine  2015;12(3):e1001801.
The infectivity of the HIV-1 acute phase has been directly measured only once, from a retrospectively identified cohort of serodiscordant heterosexual couples in Rakai, Uganda. Analyses of this cohort underlie the widespread view that the acute phase is highly infectious, even more so than would be predicted from its elevated viral load, and that transmission occurring shortly after infection may therefore compromise interventions that rely on diagnosis and treatment, such as antiretroviral treatment as prevention (TasP). Here, we re-estimate the duration and relative infectivity of the acute phase, while accounting for several possible sources of bias in published estimates, including the retrospective cohort exclusion criteria and unmeasured heterogeneity in risk.
Methods and Findings
We estimated acute phase infectivity using two approaches. First, we combined viral load trajectories and viral load-infectivity relationships to estimate infectivity trajectories over the course of infection, under the assumption that elevated acute phase infectivity is caused by elevated viral load alone. Second, we estimated the relative hazard of transmission during the acute phase versus the chronic phase (RHacute) and the acute phase duration (dacute) by fitting a couples transmission model to the Rakai retrospective cohort using approximate Bayesian computation. Our model fit the data well and accounted for characteristics overlooked by previous analyses, including individual heterogeneity in infectiousness and susceptibility and the retrospective cohort's exclusion of couples that were recorded as serodiscordant only once before being censored by loss to follow-up, couple dissolution, or study termination. Finally, we replicated two highly cited analyses of the Rakai data on simulated data to identify biases underlying the discrepancies between previous estimates and our own.
From the Rakai data, we estimated RHacute = 5.3 (95% credibility interval [95% CrI]: 0.79–57) and dacute = 1.7 mo (95% CrI: 0.55–6.8). The wide credibility intervals reflect an inability to distinguish a long, mildly infectious acute phase from a short, highly infectious acute phase, given the 10-mo Rakai observation intervals. The total additional risk, measured as excess hazard-months attributable to the acute phase (EHMacute) can be estimated more precisely: EHMacute = (RHacute - 1) × dacute, and should be interpreted with respect to the 120 hazard-months generated by a constant untreated chronic phase infectivity over 10 y of infection. From the Rakai data, we estimated that EHMacute = 8.4 (95% CrI: -0.27 to 64). This estimate is considerably lower than previously published estimates, and consistent with our independent estimate from viral load trajectories, 5.6 (95% confidence interval: 3.3–9.1). We found that previous overestimates likely stemmed from failure to account for risk heterogeneity and bias resulting from the retrospective cohort study design.
Our results reflect the interaction between the retrospective cohort exclusion criteria and high (47%) rates of censorship amongst incident serodiscordant couples in the Rakai study due to loss to follow-up, couple dissolution, or study termination. We estimated excess physiological infectivity during the acute phase from couples data, but not the proportion of transmission attributable to the acute phase, which would require data on the broader population's sexual network structure.
Previous EHMacute estimates relying on the Rakai retrospective cohort data range from 31 to 141. Our results indicate that these are substantial overestimates of HIV-1 acute phase infectivity, biased by unmodeled heterogeneity in transmission rates between couples and by inconsistent censoring. Elevated acute phase infectivity is therefore less likely to undermine TasP interventions than previously thought. Heterogeneity in infectiousness and susceptibility may still play an important role in intervention success and deserves attention in future analyses
Using simulated cohorts that account for previously unmeasured bias, Steve Bellan and colleagues provide new estimates of the duration and relative infectivity of the HIV-1 acute phase based on data from the retrospective cohort of serodiscordant couples in Rakai, Uganda.
Editors' Summary
About 35 million people are currently infected with HIV, the virus that causes AIDS, and more than 2 million people become newly infected with the virus every year, usually through having unprotected sex with an infected partner. Most people do not become ill immediately after infection, although some people develop a short flu-like illness. However, during this acute phase of infection, the amount of virus in the blood—the viral load—rises rapidly and peaks, before decreasing to a relatively stable lower level during the chronic phase of HIV infection. Chronic HIV infection, which may last for more than ten years, also has no major symptoms, but HIV slowly destroys immune system cells throughout this phase. Eventually, the immune system can no longer fight off infections by other disease-causing organisms, and HIV-positive people then develop one or more AIDS-defining conditions, including unusual infections and specific types of cancer; the HIV load also rises again during late phase infection.
Why Was This Study Done?
Antiretroviral therapy (ART) can control, but not cure, HIV infection. By decreasing the viral load, ART not only improves the health of HIV-positive individuals, but also reduces their infectiousness. Consequently, experts believe that scaling up ART could substantially reduce the rate of new HIV infections. But the successful implementation of “treatment as prevention” faces several challenges. Notably, HIV testing and treatment programs need to be widely available, and people who are HIV-positive need to adhere to ART. Another major challenge that faces treatment as prevention is that HIV transmission that occurs during the acute phase of infection is likely to evade the intervention, and it is widely accepted that HIV-positive individuals are highly infectious during this phase of infection. However, acute phase infectivity has been directly measured only once: in a retrospectively identified group of serodiscordant heterosexual couples (couples in which only one partner was HIV-positive) in Rakai, Uganda. The authors of the current study found that existing estimates of acute phase infectivity failed to take account of important aspects of the Rakai study design or of heterogeneity (variability) in infectiousness or susceptibility among the study participants. Here, the researchers use mathematical modeling to compare simulated cohorts with the Rakai data to provide new estimates of the duration and relative infectivity of the acute phase that take into account study design and heterogeneity.
What Did the Researchers Do and Find?
The researchers first used viral load trajectories and viral load–infectivity relationships to estimate infectivity trajectories over the course of infection. Using this approach, they estimated that the total additional risk attributable to the acute phase expressed as EHMacute (excess hazard-months attributable to the acute phase of infection above the hazard generated by constant untreated chronic phase infectivity) was 5.6, which is considerably lower than previous estimates (which range from 31 to 141). Next, by fitting a mathematical model designed to simulate HIV infection and transmission within couples to the Rakai data, they estimated that the relative hazard of transmission during the acute phase versus the chronic phase (RHacute) was 5.3, that the acute phase duration (dacute) was 1.7 months, and that EHMacute was 8.4. Finally, by replicating two highly cited analyses of the Rakai data on simulated data, the researchers show that the previous overestimates of acute phase infectivity likely stemmed from a failure to account for risk heterogeneity among study participants (some participants were more likely to transmit HIV or contract HIV than others because of underlying biological or behavioral differences in their infectiousness or susceptibility, respectively) and from bias arising from the retrospective cohort design of the Rakai study (serodiscordant couples who were lost to follow-up were excluded).
What Do These Findings Mean?
In common with previous estimates of acute phase infectivity, the accuracy of these findings depends on the many assumptions made by the researchers in developing their mathematical models and on the quality of the data fed into these models. Nevertheless, these findings suggest that previous estimates of the infectivity of acute phase HIV infection are substantial overestimates. Thus, the researchers suggest, elevated infectiousness early in infection alone is unlikely to undermine treatment as prevention campaigns, and the population-level benefits of treatment as prevention may be larger than predicted from earlier estimates. These revised estimates—and the impact of heterogeneity of HIV infectiousness and susceptibility to infection on HIV transmission within populations revealed by this analysis—should now be considered when designing population-scale interventions and when communicating individual-level risk of HIV transmission and infection in clinical and community settings.
Additional Information.
Please access these websites via the online version of this summary at
This study is further discussed in a PLOS Medicine Perspective by Laith J. Abu-Raddad
Information is available from the US National Institute of Allergy and Infectious Diseases on HIV infection and AIDS
NAM/aidsmap provides basic information about HIV/AIDS, information about transmission and prevention, summaries of recent research findings on HIV care and treatment, and personal stories about living with AIDS/HIV
Information is available from Avert, an international AIDS charity, on many aspects of HIV/AIDS, including detailed information on the stages of HIV infection and on treatment as prevention, and personal stories about living with HIV/AIDS
The World Health Organization provides information on all aspects of HIV/AIDS (in several languages), including its guidelines on the use of ART for treating and preventing HIV infection
The UNAIDS World AIDS Day Report 2014 provides up-to-date information about the AIDS epidemic and efforts to halt it
The PLOS Medicine Collection “Investigating the Impact of Treatment on New HIV Infections” provides more information about HIV treatment as prevention
PMCID: PMC4363602  PMID: 25781323
7.  Development of Mathematical Models for the Analysis of Hepatitis Delta Virus Viral Dynamics 
PLoS ONE  2010;5(9):e12512.
Mathematical models have shown to be extremely helpful in understanding the dynamics of different virus diseases, including hepatitis B. Hepatitis D virus (HDV) is a satellite virus of the hepatitis B virus (HBV). In the liver, production of new HDV virions depends on the presence of HBV. There are two ways in which HDV can occur in an individual: co-infection and super-infection. Co-infection occurs when an individual is simultaneously infected by HBV and HDV, while super-infection occurs in persons with an existing chronic HBV infection.
Methodology/Principal Findings
In this work a mathematical model based on differential equations is proposed for the viral dynamics of the hepatitis D virus (HDV) across different scenarios. This model takes into consideration the knowledge of the biology of the virus and its interaction with the host. In this work we will present the results of a simulation study where two scenarios were considered, co-infection and super-infection, together with different antiviral therapies. Although, in general the predicted course of HDV infection is similar to that observed for HBV, we observe a faster increase in the number of HBV infected cells and viral load. In most tested scenarios, the number of HDV infected cells and viral load values remain below corresponding predicted values for HBV.
The simulation study shows that, under the most commonly used and generally accepted therapy approaches for HDV infection, such as lamivudine (LMV) or ribavirine, peggylated alpha-interferon (IFN) or a combination of both, LMV monotherapy and combination therapy of LMV and IFN were predicted to more effectively reduce the HBV and HDV viral loads in the case of super-infection scenarios when compared with the co-infection. In contrast, IFN monotherapy was found to reduce the HDV viral load more efficiently in the case of super-infection while the effect on the HBV viral load was more pronounced during co-infection. The results suggest that there is a need for development of high efficacy therapeutic approaches towards the specific inhibition of HDV replication. These approaches may additionally be directed to the reduction of the half-life of infected cells and life-span of newly produced circulating virions.
PMCID: PMC2940762  PMID: 20862328
8.  A Hepatitis C Virus Infection Model with Time-Varying Drug Effectiveness: Solution and Analysis 
PLoS Computational Biology  2014;10(8):e1003769.
Simple models of therapy for viral diseases such as hepatitis C virus (HCV) or human immunodeficiency virus assume that, once therapy is started, the drug has a constant effectiveness. More realistic models have assumed either that the drug effectiveness depends on the drug concentration or that the effectiveness varies over time. Here a previously introduced varying-effectiveness (VE) model is studied mathematically in the context of HCV infection. We show that while the model is linear, it has no closed-form solution due to the time-varying nature of the effectiveness. We then show that the model can be transformed into a Bessel equation and derive an analytic solution in terms of modified Bessel functions, which are defined as infinite series, with time-varying arguments. Fitting the solution to data from HCV infected patients under therapy has yielded values for the parameters in the model. We show that for biologically realistic parameters, the predicted viral decay on therapy is generally biphasic and resembles that predicted by constant-effectiveness (CE) models. We introduce a general method for determining the time at which the transition between decay phases occurs based on calculating the point of maximum curvature of the viral decay curve. For the parameter regimes of interest, we also find approximate solutions for the VE model and establish the asymptotic behavior of the system. We show that the rate of second phase decay is determined by the death rate of infected cells multiplied by the maximum effectiveness of therapy, whereas the rate of first phase decline depends on multiple parameters including the rate of increase of drug effectiveness with time.
Author Summary
Fitting simple models of therapy for viral diseases, such as hepatitis C virus (HCV) or human immunodeficiency virus, to patient data has yielded significant insights into the underlying viral dynamics. In general, these models assume that, once therapy is started, the drug has a constant effectiveness. More realistic assumptions are that drug effectiveness either depends directly on the drug concentration or varies over time. Here a previously introduced varying-effectiveness (VE) differential equation model is studied in the context of HCV infection. We show that the previously-unsolved VE model can be transformed into a Bessel equation and derive an analytic solution in terms of modified Bessel functions with time-varying arguments. These analytic solutions can be more readily used to fit the model to patient data than the underlying differential equations. We also find approximate solutions and establish the asymptotic behavior of the system. Typically viral load measurements exhibit a biphasic decline after therapy initiation. We show that the rate of second phase decay is determined by the death rate of infected cells multiplied by the maximum effectiveness of therapy, whereas the rate of first phase decline may depend on multiple parameters, resulting in differing first phase declines across various HCV therapies.
PMCID: PMC4125050  PMID: 25101902
9.  On the Laws of Virus Spread through Cell Populations 
Journal of Virology  2014;88(22):13240-13248.
The dynamics of viral infections have been investigated extensively, often with a combination of experimental and mathematical approaches. Mathematical descriptions of virus spread through cell populations are well established in the literature and have yielded important insights, yet the formulation of certain fundamental aspects of virus dynamics models remains uncertain and untested. Here, we investigate the process of infection and, in particular, the effect of varying the target cell population size on the number of productively infected cells generated. Using an in vitro single-round HIV-1 infection system, we find that the established modeling framework cannot accurately fit the data. If the model is fit to data with the lowest number of cells and is used to predict data generated with larger cell populations, the model significantly overestimates the number of productively infected cells generated. Interestingly, this deviation becomes stronger under experimental conditions that promote mixing of cells and viruses. The reason for the deviation is that the standard model makes certain oversimplifying assumptions about the fate of viruses that fail to find a cell in their immediate proximity. We derive from stochastic processes a different model that assumes simultaneous access of the virus to multiple target cells. In this scenario, if no cell is available to the virus at its location, it has a chance to interact with other cells, a process that can be promoted by mixing of the populations. This model can accurately fit the experimental data and suggests a new interpretation of mass action in virus dynamics models.
IMPORTANCE Understanding the principles of virus growth through cell populations is of fundamental importance to virology. It helps us make informed decisions about intervention strategies aimed at preventing virus growth, such as drug treatment or vaccination approaches, e.g., in HIV infection, yet considerable uncertainty remains in this respect. An important variable in this context is the number of susceptible cells available for virus replication. How does the number of susceptible cells influence the growth potential of the virus? Besides the importance of such information for clinical responses, a thorough understanding of this is also important for the prediction of virus levels in patients and the estimation of crucial patient parameters through the use of mathematical models. This paper investigates the relationship between target cell availability and the virus growth potential with a combination of experimental and mathematical approaches and provides significant new insights.
PMCID: PMC4249084  PMID: 25187551
10.  Kinetics of Coinfection with Influenza A Virus and Streptococcus pneumoniae 
PLoS Pathogens  2013;9(3):e1003238.
Secondary bacterial infections are a leading cause of illness and death during epidemic and pandemic influenza. Experimental studies suggest a lethal synergism between influenza and certain bacteria, particularly Streptococcus pneumoniae, but the precise processes involved are unclear. To address the mechanisms and determine the influences of pathogen dose and strain on disease, we infected groups of mice with either the H1N1 subtype influenza A virus A/Puerto Rico/8/34 (PR8) or a version expressing the 1918 PB1-F2 protein (PR8-PB1-F2(1918)), followed seven days later with one of two S. pneumoniae strains, type 2 D39 or type 3 A66.1. We determined that, following bacterial infection, viral titers initially rebound and then decline slowly. Bacterial titers rapidly rise to high levels and remain elevated. We used a kinetic model to explore the coupled interactions and study the dominant controlling mechanisms. We hypothesize that viral titers rebound in the presence of bacteria due to enhanced viral release from infected cells, and that bacterial titers increase due to alveolar macrophage impairment. Dynamics are affected by initial bacterial dose but not by the expression of the influenza 1918 PB1-F2 protein. Our model provides a framework to investigate pathogen interaction during coinfections and to uncover dynamical differences based on inoculum size and strain.
Author Summary
Influenza virus infected individuals often become coinfected with a bacterial pathogen and, consequently, morbidity and mortality are significantly increased. A better understanding of how these pathogens interact with each other and the host is of key importance. Here, we use data from infected mice together with mathematical modeling and quantitative analyses to understand how each pathogen influences the other, and how the 1918 influenza PB1-F2 protein and the bacterial strain and dose contribute to coinfection kinetics. We find that influenza viral titers increase when Streptococcus pneumoniae is present and that the bacteria establish and grow rapidly when influenza is present. Our model and analyses suggest that the influenza infection reduces the bacterial clearance ability of alveolar macrophages and that the subsequent S. pneumoniae infection enhances viral release from infected cells. These results provide new insights into the mechanisms of influenza coinfection and the differences in pathogenesis of influenza and S. pneumoniae strains.
PMCID: PMC3605146  PMID: 23555251
11.  Simple Mathematical Models Do Not Accurately Predict Early SIV Dynamics 
Viruses  2015;7(3):1189-1217.
Upon infection of a new host, human immunodeficiency virus (HIV) replicates in the mucosal tissues and is generally undetectable in circulation for 1–2 weeks post-infection. Several interventions against HIV including vaccines and antiretroviral prophylaxis target virus replication at this earliest stage of infection. Mathematical models have been used to understand how HIV spreads from mucosal tissues systemically and what impact vaccination and/or antiretroviral prophylaxis has on viral eradication. Because predictions of such models have been rarely compared to experimental data, it remains unclear which processes included in these models are critical for predicting early HIV dynamics. Here we modified the “standard” mathematical model of HIV infection to include two populations of infected cells: cells that are actively producing the virus and cells that are transitioning into virus production mode. We evaluated the effects of several poorly known parameters on infection outcomes in this model and compared model predictions to experimental data on infection of non-human primates with variable doses of simian immunodifficiency virus (SIV). First, we found that the mode of virus production by infected cells (budding vs. bursting) has a minimal impact on the early virus dynamics for a wide range of model parameters, as long as the parameters are constrained to provide the observed rate of SIV load increase in the blood of infected animals. Interestingly and in contrast with previous results, we found that the bursting mode of virus production generally results in a higher probability of viral extinction than the budding mode of virus production. Second, this mathematical model was not able to accurately describe the change in experimentally determined probability of host infection with increasing viral doses. Third and finally, the model was also unable to accurately explain the decline in the time to virus detection with increasing viral dose. These results suggest that, in order to appropriately model early HIV/SIV dynamics, additional factors must be considered in the model development. These may include variability in monkey susceptibility to infection, within-host competition between different viruses for target cells at the initial site of virus replication in the mucosa, innate immune response, and possibly the inclusion of several different tissue compartments. The sobering news is that while an increase in model complexity is needed to explain the available experimental data, testing and rejection of more complex models may require more quantitative data than is currently available.
PMCID: PMC4379566  PMID: 25781919
early SIV/HIV infection; mathematical model; eclipse phase; stochastic; Gillespie algorithm
12.  The dependence of viral parameter estimates on the assumed viral life cycle: limitations of studies of viral load data. 
Estimation of viral parameters, such as the basic reproductive number (R0) and infected cell life span, is central to the quantitative study of the within-host dynamics of viral diseases such as human immunodeficiency virus, hepatitis B or hepatitis C. As these parameters can rarely be determined directly, they are usually estimated indirectly by fitting mathematical models to viral load data. This paper investigates how parameter estimates obtained by such procedures depend on the assumptions made concerning the viral life cycle. It finds that estimates of the basic reproductive number obtained using viral load data collected during the initial stages of infection can depend quite sensitively on these assumptions. The use of models which neglect the intracellular delay before virion production can lead to severe underestimates of R0 and, hence, to overly optimistic predictions of how efficacious treatment must be in order to prevent or eradicate the disease. These results are also of importance for attempts at estimating R0 from similar epidemiological data as there is a correspondence between within-host and between-host models. Estimates of the life span of infected cells obtained from viral load data collected during drug treatment studies also depend on the assumptions made in modelling the virus life cycle. The use of more realistic descriptions of the life cycle is seen to increase estimates of infected cell life span, in addition to providing a new explanation for the shoulder phase seen during drug treatment. This study highlights the limitations of what can be learnt by fitting mathematical models to infectious disease data without detailed independent knowledge of the life cycle of the infectious agent.
PMCID: PMC1088679  PMID: 11345331
13.  Effect of different modes of viral spread on the dynamics of multiply infected cells in human immunodeficiency virus infection 
Infection of individual cells with more than one HIV particle is an important feature of HIV replication, which may contribute to HIV pathogenesis via the occurrence of recombination, viral complementation and other outcomes that influence HIV replication and evolutionary dynamics. A previous mathematical model of co-infection has shown that the number of cells infected with i viruses correlates with the ith power of the singly infected cell population, and this has partly been observed in experiments. This model, however, assumed that virus spread from cell to cell occurs only via free virus particles, and that viruses and cells mix perfectly. Here, we introduce a cellular automaton model that takes into account different modes of virus spread among cells, including cell to cell transmission via the virological synapse, and spatially constrained virus spread. In these scenarios, it is found that the number of multiply infected cells correlates linearly with the number of singly infected cells, meaning that co-infection plays a greater role at lower virus loads. The model further indicates that current experimental systems that are used to study co-infection dynamics fail to reflect the true dynamics of multiply infected cells under these specific assumptions, and that new experimental techniques need to be designed to distinguish between the different assumptions.
PMCID: PMC3033025  PMID: 20659927
HIV; multiple infection; mathematical model; spatial; virus spread
14.  Intracellular transactivation of HIV can account for the decelerating decay of virus load during drug therapy 
Linking the intracellular transactivation circuit of HIV into a virus dynamics model can account for activation of infected cells and reversion into latency.We hypothesize that the activation of latently infected cells is governed by the basal transcription rate of the integrated provirus rather than through extracellular stimuli.This systems approach to modelling virus dynamics offers a promising framework to infer the extracellular dynamics of cell populations from their intracellular reaction networks.
The viral reservoir of latently infected cells is considered to be one of the major barriers for eradicating the virus from patients infected with HIV. During prolonged antiretroviral therapy, it has been shown that the pool of latently infected cells decays very slowly and at a decreasing rate. The underlying mechanisms causing this decelerating decay are still unclear (Lassen et al, 2004a, 2004b; Han et al, 2007). A recent study has shown that HIV can exhibit a switch-like behavior where infected cells can either be activated or become resting in a latent state (Weinberger et al, 2005). To investigate the effect of this switch-like behavior on the viral infection dynamics, we devise a new model that links the intracellular transactivation of the virus with the extracellular virus dynamics (Box 1). The model can explain the typical decelerating decay of HIV that is observed during antiretroviral therapy. We find that the activation of latently infected cells is governed by the basal transcription rate of the inserted provirus. Therefore, our analysis suggests that increasing the basal transcription rate of the HIV provirus could serve as a new therapeutic intervention for eradicating the pool of latently infected cells. In addition, our systems approach to modeling virus dynamics offers a promising framework for inferring the extracellular dynamics of cell populations from their intracellular reaction networks.
Basic virus dynamics models have been essential in understanding quantitative issues of HIV replication. However, several parts of the viral life cycle remain elusive. One of the most critical steps is the start of viral transcription, which is governed by the regulatory protein trans-activator of transcription (Tat) that induces a positive feedback loop. It has been shown that this feedback loop can alternate between two states leading to a transient activation of viral transcription. Using Monte Carlo simulations, we integrate the transactivation circuit into a new virus dynamics model having an age-dependent transactivation rate and reversion into latency. The cycling of infected cells between an activated and latent state results in the typical decelerating decay of virus load following therapy. Further, we hypothesize that the activation of latently infected cells is governed by the basal transcription rate of the integrated provirus rather than the intra- or extracellular environment. Finally, our systems approach to modeling virus dynamics offers a promising framework to infer the extracellular dynamics of cell populations from their intracellular reaction networks.
PMCID: PMC2835566  PMID: 20160709
HIV; mathematical modeling; stochastic noise; transactivation; viral latency
15.  Reducing the Impact of the Next Influenza Pandemic Using Household-Based Public Health Interventions 
PLoS Medicine  2006;3(9):e361.
The outbreak of highly pathogenic H5N1 influenza in domestic poultry and wild birds has caused global concern over the possible evolution of a novel human strain [1]. If such a strain emerges, and is not controlled at source [2,3], a pandemic is likely to result. Health policy in most countries will then be focused on reducing morbidity and mortality.
Methods and Findings
We estimate the expected reduction in primary attack rates for different household-based interventions using a mathematical model of influenza transmission within and between households. We show that, for lower transmissibility strains [2,4], the combination of household-based quarantine, isolation of cases outside the household, and targeted prophylactic use of anti-virals will be highly effective and likely feasible across a range of plausible transmission scenarios. For example, for a basic reproductive number (the average number of people infected by a typically infectious individual in an otherwise susceptible population) of 1.8, assuming only 50% compliance, this combination could reduce the infection (symptomatic) attack rate from 74% (49%) to 40% (27%), requiring peak quarantine and isolation levels of 6.2% and 0.8% of the population, respectively, and an overall anti-viral stockpile of 3.9 doses per member of the population. Although contact tracing may be additionally effective, the resources required make it impractical in most scenarios.
National influenza pandemic preparedness plans currently focus on reducing the impact associated with a constant attack rate, rather than on reducing transmission. Our findings suggest that the additional benefits and resource requirements of household-based interventions in reducing average levels of transmission should also be considered, even when expected levels of compliance are only moderate.
Voluntary household-based quarantine and external isolation are likely to be effective in limiting the morbidity and mortality of an influenza pandemic, even if such a pandemic cannot be entirely prevented, and even if compliance with these interventions is moderate.
Editors' Summary
Naturally occurring variation in the influenza virus can lead both to localized annual epidemics and to less frequent global pandemics of catastrophic proportions. The most destructive of the three influenza pandemics of the 20th century, the so-called Spanish flu of 1918–1919, is estimated to have caused 20 million deaths. As evidenced by ongoing tracking efforts and news media coverage of H5N1 avian influenza, contemporary approaches to monitoring and communications can be expected to alert health officials and the general public of the emergence of new, potentially pandemic strains before they spread globally.
Why Was This Study Done?
In order to act most effectively on advance notice of an approaching influenza pandemic, public health workers need to know which available interventions are likely to be most effective. This study was done to estimate the effectiveness of specific preventive measures that communities might implement to reduce the impact of pandemic flu. In particular, the study evaluates methods to reduce person-to-person transmission of influenza, in the likely scenario that complete control cannot be achieved by mass vaccination and anti-viral treatment alone.
What Did the Researchers Do and Find?
The researchers developed a mathematical model—essentially a computer simulation—to simulate the course of pandemic influenza in a hypothetical population at risk for infection at home, through external peer networks such as schools and workplaces, and through general community transmission. Parameters such as the distribution of household sizes, the rate at which individuals develop symptoms from nonpandemic viruses, and the risk of infection within households were derived from demographic and epidemiologic data from Hong Kong, as well as empirical studies of influenza transmission. A model based on these parameters was then used to calculate the effects of interventions including voluntary household quarantine, voluntary individual isolation in a facility outside the home, and contact tracing (that is, asking infectious individuals to identify people whom they may have infected and then warning those people) on the spread of pandemic influenza through the population. The model also took into account the anti-viral treatment of exposed, asymptomatic household members and of individuals in isolation, and assumed that all intervention strategies were put into place before the arrival of individuals infected with the pandemic virus.
  Using this model, the authors predicted that even if only half of the population were to comply with public health interventions, the proportion infected during the first year of an influenza pandemic could be substantially reduced by a combination of household-based quarantine, isolation of actively infected individuals in a location outside the household, and targeted prophylactic treatment of exposed individuals with anti-viral drugs. Based on an influenza-associated mortality rate of 0.5% (as has been estimated for New York City in the 1918–1919 pandemic), the magnitude of the predicted benefit of these interventions is a reduction from 49% to 27% in the proportion of the population who become ill in the first year of the pandemic, which would correspond to 16,000 fewer deaths in a city the size of Hong Kong (6.8 million people). In the model, anti-viral treatment appeared to be about as effective as isolation when each was used in combination with household quarantine, but would require stockpiling 3.9 doses of anti-viral for each member of the population. Contact tracing was predicted to provide a modest additional benefit over quarantine and isolation, but also to increase considerably the proportion of the population in quarantine.
What Do These Findings Mean?
This study predicts that voluntary household-based quarantine and external isolation can be effective in limiting the morbidity and mortality of an influenza pandemic, even if such a pandemic cannot be entirely prevented, and even if compliance with these interventions is far from uniform. These simulations can therefore inform preparedness plans in the absence of data from actual intervention trials, which would be impossible outside (and impractical within) the context of an actual pandemic. Like all mathematical models, however, the one presented in this study relies on a number of assumptions regarding the characteristics and circumstances of the situation that it is intended to represent. For example, the authors found that the efficacy of policies to reduce the rate of infection vary according to the ease with which a given virus spreads from person to person. Because this parameter (known as the basic reproductive ratio, R0) cannot be reliably predicted for a new viral strain based on past epidemics, the authors note that in an actual influenza pandemic rapid determinations of R0 in areas already involved would be necessary to finalize public health responses in threatened areas. Further, the implementation of the interventions that appear beneficial in this model would require devoting attention and resources to practical considerations, such as how to staff isolation centers and provide food and water to those in household quarantine. However accurate the scientific data and predictive models may be, their effectiveness can only be realized through well-coordinated local, as well as international, efforts.
Additional Information.
Please access these Web sites via the online version of this summary at
• World Health Organization influenza pandemic preparedness page
• US Department of Health and Human Services avian and pandemic flu information site
• Pandemic influenza page from the Public Health Agency of Canada
• Emergency planning page on pandemic flu from the England Department of Health
• Wikipedia entry on pandemic influenza with links to individual country resources (note: Wikipedia is a free Internet encyclopedia that anyone can edit)
PMCID: PMC1526768  PMID: 16881729
16.  Improving the estimation of the death rate of infected cells from time course data during the acute phase of virus infections: application to acute HIV-1 infection in a humanized mouse model 
Mathematical modeling of virus dynamics has provided quantitative insights into viral infections such as influenza, the simian immunodeficiency virus/human immunodeficiency virus, hepatitis B, and hepatitis C. Through modeling, we can estimate the half-life of infected cells, the exponential growth rate, and the basic reproduction number (R0). To calculate R0 from virus load data, the death rate of productively infected cells is required. This can be readily estimated from treatment data collected during the chronic phase, but is difficult to determine from acute infection data. Here, we propose two new models that can reliably estimate the average life span of infected cells from acute-phase data, and apply both methods to experimental data from humanized mice infected with HIV-1.
Both new models, called as the reduced quasi-steady state (RQS) model and the piece-wise regression (PWR) model, are derived by simplification of a standard model for the acute-phase dynamics of target cells, viruses and infected cells. By having only a limited number of parameters, both models allow us to reliably estimate the death rate of productively infected cells. Simulated datasets with plausible parameter values are generated with the standard model to compare the performance of the new models with that of the major previous model (i.e., the simple exponential model). Finally, we fit models to time course data from HIV-1 infected humanized mice to estimate the several important parameters characterizing their acute infection.
Results and conclusions
The new models provided much better estimates than the previous model because they more precisely capture the de novo infection process. Both models describe the acute phase of HIV-1 infected humanized mice reasonably well, and we estimated an average death rate of infected cells of 0.61 and 0.61, an average exponential growth rate of 0.69 and 0.76, and an average basic reproduction number of 2.30 and 2.38 in the RQS model and the PWR model, respectively. These estimates are fairly close to those obtained in humans.
PMCID: PMC4035760  PMID: 24885827
Population dynamics model; Parameter estimation; Virus infection dynamics; Death rate of infected cells; HIV-1; Humanized mouse model
17.  Infection of HIV-specific CD4 T helper cells and the clonal composition of the response 
Journal of theoretical biology  2012;304:143-151.
A hallmark of human immunodeficiency virus is its ability to infect CD4+ T helper cells, thus impairing helper cell responses and consequently effector responses whose maintenance depends on help (such as killer T cells and B cells). In particular, the virus has been shown to infect HIV specific helper cells preferentially. Using mathematical models, we investigate the consequence of this assumption for the basic dynamics between HIV and its target cells, assuming the existence of two independently regulated helper cell clones, directed against different epitopes of the virus. In contrast to previous studies, we examine a relatively simple scenario, only concentrating on the interactions between the virus and its target cells, not taking into account any helper-dependent effector responses. Further, there is no direct competition for space or antigenic stimulation in the model. Yet, a set of interesting outcomes is observed that provide further insights into factors that shape helper cell responses. Despite the absence of competition, a stronger helper cell clone can still exclude a weaker one because the two clones are infected by the same pathogen, an ecological concept called “apparent competition”. Moreover, we also observe “facilitation”: if one of the helper cell clones is too weak to become established in isolation, the presence of a stronger clone can provide enhanced antigenic stimulation, thus allowing the weaker clone to persist. The dependencies of these outcomes on parameters is explored. Factors that reduce viral infectivity and increase the death rate of infected cells promote coexistence, which is in agreement with the observation that stronger immunity correlates with broader helper cell responses. The basic model is extended to explicitly take into account helper-dependent CTL responses and direct competition. This study sheds further light onto the factors that can influence the clonal composition of HIV-specific helper cell responses, which has implications for the overall pattern of disease progression.
PMCID: PMC4082790  PMID: 22480435
18.  HIV Treatment as Prevention: Systematic Comparison of Mathematical Models of the Potential Impact of Antiretroviral Therapy on HIV Incidence in South Africa 
PLoS Medicine  2012;9(7):e1001245.
Many mathematical models have investigated the impact of expanding access to antiretroviral therapy (ART) on new HIV infections. Comparing results and conclusions across models is challenging because models have addressed slightly different questions and have reported different outcome metrics. This study compares the predictions of several mathematical models simulating the same ART intervention programmes to determine the extent to which models agree about the epidemiological impact of expanded ART.
Methods and Findings
Twelve independent mathematical models evaluated a set of standardised ART intervention scenarios in South Africa and reported a common set of outputs. Intervention scenarios systematically varied the CD4 count threshold for treatment eligibility, access to treatment, and programme retention. For a scenario in which 80% of HIV-infected individuals start treatment on average 1 y after their CD4 count drops below 350 cells/µl and 85% remain on treatment after 3 y, the models projected that HIV incidence would be 35% to 54% lower 8 y after the introduction of ART, compared to a counterfactual scenario in which there is no ART. More variation existed in the estimated long-term (38 y) reductions in incidence. The impact of optimistic interventions including immediate ART initiation varied widely across models, maintaining substantial uncertainty about the theoretical prospect for elimination of HIV from the population using ART alone over the next four decades. The number of person-years of ART per infection averted over 8 y ranged between 5.8 and 18.7. Considering the actual scale-up of ART in South Africa, seven models estimated that current HIV incidence is 17% to 32% lower than it would have been in the absence of ART. Differences between model assumptions about CD4 decline and HIV transmissibility over the course of infection explained only a modest amount of the variation in model results.
Mathematical models evaluating the impact of ART vary substantially in structure, complexity, and parameter choices, but all suggest that ART, at high levels of access and with high adherence, has the potential to substantially reduce new HIV infections. There was broad agreement regarding the short-term epidemiologic impact of ambitious treatment scale-up, but more variation in longer term projections and in the efficiency with which treatment can reduce new infections. Differences between model predictions could not be explained by differences in model structure or parameterization that were hypothesized to affect intervention impact.
Please see later in the article for the Editors' Summary
Editors' Summary
Following the first reported case of AIDS in 1981, the number of people infected with HIV, the virus that causes AIDS, increased rapidly. In recent years, the number of people becoming newly infected has declined slightly, but the virus continues to spread at unacceptably high levels. In 2010 alone, 2.7 million people became HIV-positive. HIV, which is usually transmitted through unprotected sex, destroys CD4 lymphocytes and other immune system cells, leaving infected individuals susceptible to other infections. Early in the AIDS epidemic, half of HIV-infected people died within eleven years of infection. Then, in 1996, antiretroviral therapy (ART) became available, and, for people living in affluent countries, HIV/AIDS gradually became considered a chronic condition. But because ART was expensive, for people living in developing countries HIV/AIDS remained a fatal condition. Roll-out of ART in developing countries first started in the early 2000s. In 2006, the international community set a target of achieving universal ART coverage by 2010. Although this target has still not been reached, by the end of 2010, 6.6 million of the estimated 15 million people in need of ART in developing countries were receiving ART.
Why Was This Study Done?
Several studies suggest that ART, in addition to reducing illness and death among HIV-positive people, reduces HIV transmission. Consequently, there is interest in expanding the provision of ART as a strategy for reducing the spread of HIV (“HIV treatment as prevention"), particularly in sub-Saharan Africa, where one in 20 adults is HIV-positive. It is important to understand exactly how ART might contribute to averting HIV transmission. Several mathematical models that simulate HIV infection and disease progression have been developed to investigate the impact of expanding access to ART on the incidence of HIV (the number of new infections occurring in a population over a year). But, although all these models predict that increased ART coverage will have epidemiologic (population) benefits, they vary widely in their estimates of the magnitude of these benefits. In this study, the researchers systematically compare the predictions of 12 mathematical models of the HIV epidemic in South Africa, simulating the same ART intervention programs to determine the extent to which different models agree about the impact of expanded ART.
What Did the Researchers Do and Find?
The researchers invited groups who had previously developed mathematical models of the epidemiological impact of expanded access to ART in South Africa to participate in a systematic comparison exercise in which their models were used to simulate ART scale-up scenarios in which the CD4 count threshold for treatment eligibility, access to treatment, and retention on treatment were systematically varied. To exclude variation resulting from different model assumptions about the past and current ART program, it was assumed that ART is introduced into the population in the year 2012, with no treatment provision prior to this, and interventions were evaluated in comparison to an artificial counterfactual scenario in which no treatment is provided. A standard scenario based on the World Health Organization's recommended threshold for initiation of ART, although unrepresentative of current provision in South Africa, was used to compare the models. In this scenario, 80% of HIV-infected individuals received treatment, they started treatment on average a year after their CD4 count dropped below 350 cells per microliter of blood, and 85% remained on treatment after three years. The models predicted that, with a start point of 2012, the HIV incidence would be 35%–54% lower in 2020 and 32%–74% lower in 2050 compared to a counterfactual scenario where there was no ART. Estimates of the number of person-years of ART needed per infection averted (the efficiency with which ART reduced new infections) ranged from 6.3–18.7 and from 4.5–20.2 over the periods 2012–2020 and 2012–2050, respectively. Finally, estimates of the impact of ambitious interventions (for example, immediate treatment of all HIV-positive individuals) varied widely across the models.
What Do These Findings Mean?
Although the mathematical models used in this study had different characteristics, all 12 predict that ART, at high levels of access and adherence, has the potential to reduce new HIV infections. However, although the models broadly agree about the short-term epidemiologic impact of treatment scale-up, their longer-term projections (including whether ART alone can eliminate HIV infection) and their estimates of the efficiency with which ART can reduce new infections vary widely. Importantly, it is possible that all these predictions will be wrong—all the models may have excluded some aspect of HIV transmission that will be found in the future to be crucial. Finally, these findings do not aim to indicate which specific ART interventions should be used to reduce the incidence of HIV. Rather, by comparing the models that are being used to investigate the feasibility of “HIV treatment as prevention," these findings should help modelers and policy-makers think critically about how the assumptions underlying these models affect the models' predictions.
Additional Information
Please access these websites via the online version of this summary at
This study is part of the July 2012 PLoS Medicine Collection, Investigating the Impact of Treatment on New HIV Infections
Information is available from the US National Institute of Allergy and Infectious Diseases on HIV infection and AIDS
NAM/aidsmap provides basic information about HIV/AIDS and summaries of recent research findings on HIV care and treatment
Information is available from Avert, an international AIDS charity on many aspects of HIV/AIDS, including information on HIV/AIDS treatment and care, on HIV treatment as prevention, and on HIV/AIDS in South Africa (in English and Spanish)
The World Health Organization provides information about universal access to AIDS treatment (in English, French, and Spanish); its 2010 ART guidelines can be downloaded
The HIV Modelling Consortium aims to improve scientific support for decision-making by coordinating mathematical modeling of the HIV epidemic
Patient stories about living with HIV/AIDS are available through Avert; the charity website Healthtalkonline also provides personal stories about living with HIV, including stories about taking anti-HIV drugs and the challenges of anti-HIV drugs
PMCID: PMC3393664  PMID: 22802730
19.  Modeling Within-Host Dynamics of Influenza Virus Infection Including Immune Responses 
PLoS Computational Biology  2012;8(6):e1002588.
Influenza virus infection remains a public health problem worldwide. The mechanisms underlying viral control during an uncomplicated influenza virus infection are not fully understood. Here, we developed a mathematical model including both innate and adaptive immune responses to study the within-host dynamics of equine influenza virus infection in horses. By comparing modeling predictions with both interferon and viral kinetic data, we examined the relative roles of target cell availability, and innate and adaptive immune responses in controlling the virus. Our results show that the rapid and substantial viral decline (about 2 to 4 logs within 1 day) after the peak can be explained by the killing of infected cells mediated by interferon activated cells, such as natural killer cells, during the innate immune response. After the viral load declines to a lower level, the loss of interferon-induced antiviral effect and an increased availability of target cells due to loss of the antiviral state can explain the observed short phase of viral plateau in which the viral level remains unchanged or even experiences a minor second peak in some animals. An adaptive immune response is needed in our model to explain the eventual viral clearance. This study provides a quantitative understanding of the biological factors that can explain the viral and interferon kinetics during a typical influenza virus infection.
Author Summary
Influenza, commonly referred to as the flu, is a contagious respiratory illness caused by influenza virus infections. Although most infected subjects with intact immune systems are able to clear the virus without developing serious flu complications, the mechanisms underlying viral control are not fully understood. In this paper, we address this question by developing mathematical models that include both innate and adaptive immune responses, and fitting them to experimental data from horses infected with equine influenza virus. We find that the innate immune response, such as natural killer cell-mediated infected cell killing and interferon's antiviral effect, can explain the first rapid viral decline and subsequent second peak viremia, and that the adaptive immune response is needed to eventually clear the virus. This study improves our understanding of influenza virus dynamics and may provide more information for future research in influenza pathogenesis, treatment, and vaccination.
PMCID: PMC3386161  PMID: 22761567
20.  Experimentally guided models reveal replication principles that shape the mutation distribution of RNA viruses 
eLife  null;4:e03753.
Life history theory posits that the sequence and timing of events in an organism's lifespan are fine-tuned by evolution to maximize the production of viable offspring. In a virus, a life history strategy is largely manifested in its replication mode. Here, we develop a stochastic mathematical model to infer the replication mode shaping the structure and mutation distribution of a poliovirus population in an intact single infected cell. We measure production of RNA and poliovirus particles through the infection cycle, and use these data to infer the parameters of our model. We find that on average the viral progeny produced from each cell are approximately five generations removed from the infecting virus. Multiple generations within a single cell infection provide opportunities for significant accumulation of mutations per viral genome and for intracellular selection.
eLife digest
Viruses with genetic information made up of molecules of RNA can multiply quickly, but not very accurately. This means that many errors, or mutations, occur when the RNA is copied to create new viruses. The advantage of this rapid, but mistake-filled, RNA replication process is that some of the mutations will be beneficial to the virus. This allows viruses to rapidly evolve, for example, to develop resistance against drugs.
The poliovirus is an RNA virus that can cause paralysis and death in humans. To prevent such infections, scientists have extensively studied the poliovirus and have developed effective vaccines against it that have eliminated the virus from all but a few countries. Because so much is known about the poliovirus and because it has a very simple structure, scientists continue to use the poliovirus as a model to study virus behavior.
One unknown aspect of the poliovirus' behavior is how it replicates after invading a cell. Are all of its RNA copies made from the original viral RNA that first infected the cell, in what is known as a ‘stamping machine’ model? Or do the new copies of the RNA also get copied themselves in a ‘geometric replication mode’ that increases the likelihood of mutations and enables the virus to evolve more rapidly?
Viral RNA molecules are copied by one of the virus's own proteins and so before the viral RNA can be replicated, it must first be translated to form viral proteins. When and where replication begins depends on the concentration of translated proteins around the RNA and so replication tends to begin in particular areas of the cell at different times. Schulte, Draghi et al. used mathematical modeling to create computer simulations of the number of polioviruses in a cell that take into account these time and space constraints. By including random elements in the model, the simulated behavior more accurately follows experimentally recorded data than previously used models.
The results of the model led Schulte, Draghi et al. to conclude that the poliovirus replicates by the ‘geometric mode’; as new copies of the poliovirus RNA are made, each copy goes on to make more copies. This means that in a single infected cell there are multiple generations of RNA, and each generation may undergo distinct mutations that are passed on to the next set of RNA copies. In fact, Schulte, Draghi et al. found that the average virus released from an infected cell is the great-great-great-granddaughter of the original virus that infected the cell. With so many different generations of virus coexisting in a cell, there are a lot of opportunities for new genetic combinations to occur and for viruses to evolve new abilities.
PMCID: PMC4311501  PMID: 25635405
stochastic mathematical modeling; population structure; virus replication; RNA replication; viruses
21.  Reassessing the Human Immunodeficiency Virus Type 1 Life Cycle through Age-Structured Modeling: Life Span of Infected Cells, Viral Generation Time, and Basic Reproductive Number, R0▿ † 
Journal of Virology  2009;83(15):7659-7667.
The rapid decay of the viral load after drug treatment in patients infected with human immunodeficiency virus type 1 (HIV-1) has been shown to result from the rapid loss of infected cells due to their high turnover, with a generation time of around 1 to 2 days. Traditionally, viral decay dynamics after drug treatment is investigated using models of differential equations in which both the death rate of infected cells and the viral production rate are assumed to be constant. Here, we describe age-structured models of the viral decay dynamics in which viral production rates and death rates depend on the age of the infected cells. In order to investigate the effects of age-dependent rates, we compared these models with earlier descriptions of the viral load decay and fitted them to previously published data. We have found no supporting evidence that infected-cell death rates increase, but cannot reject the possibility that viral production rates increase, with the age of the cells. In particular, we demonstrate that an exponential increase in viral production with infected-cell age is perfectly consistent with the data. Since an exponential increase in virus production can compensate for the exponential loss of infected cells, the death rates of HIV-1-infected cells may be higher than previously anticipated. We discuss the implications of these findings for the life span of infected cells, the viral generation time, and the basic reproductive number, R0.
PMCID: PMC2708627  PMID: 19457999
22.  Viral and Latent Reservoir Persistence in HIV-1–Infected Patients on Therapy 
PLoS Computational Biology  2006;2(10):e135.
Despite many years of potent antiretroviral therapy, latently infected cells and low levels of plasma virus have been found to persist in HIV-infected patients. The factors influencing this persistence and their relative contributions have not been fully elucidated and remain controversial. Here, we address these issues by developing and employing a simple, but mechanistic viral dynamics model. The model has two novel features. First, it assumes that latently infected T cells can undergo bystander proliferation without transitioning into active viral production. Second, it assumes that the rate of latent cell activation decreases with time on antiretroviral therapy due to the activation and subsequent loss of latently infected cells specific for common antigens, leaving behind cells that are successively less frequently activated. Using the model, we examined the quantitative contributions of T cell bystander proliferation, latent cell activation, and ongoing viral replication to the stability of the latent reservoir and persisting low-level viremia. Not surprisingly, proliferation of latently infected cells helped maintain the latent reservoir in spite of loss of latent infected cells through activation and death, and affected viral dynamics to an extent that depended on the magnitude of latent cell activation. In the limit of zero latent cell activation, the latent cell pool and viral load became uncoupled. However, as the activation rate increased, the plasma viral load could be maintained without depleting the latent reservoir, even in the absence of viral replication. The influence of ongoing viral replication on the latent reservoir remained insignificant for drug efficacies above the “critical efficacy” irrespective of the activation rate. However, for lower drug efficacies viral replication enabled the stable maintenance of both the latent reservoir and the virus. Our model and analysis methods provide a quantitative and qualitative framework for probing how different viral and host factors contribute to the dynamics of the latent reservoir and the virus, offering new insights into the principal determinants of their persistence.
Antiretroviral therapy has greatly reduced the mortality of HIV-infected patients. However, even after a decade of suppressive therapy, low levels of virus and latent viral reservoirs persist. Flushing out these reservoirs is a major hurdle that remains to be overcome by anti-HIV therapy. Despite many years of extensive studies, we still lack quantitative understanding of the factors that maintain viral reservoirs and prevent a cure of HIV infection. In this paper, Kim and Perelson develop a novel mathematical model that incorporates the possibility that latently infected cells, like other memory cells, undergo bystander proliferation without being activated. Using the model, they show that T cell bystander proliferation, combined with latent cell activation, enables the stable maintenance of both the latent reservoir and the virus, even in the absence of viral replication. Further, they show that the influence of ongoing viral replication on maintaining the latent reservoir remains relatively insignificant for a range of high drug efficacies. Their results suggest that if the long-term persistence of the latent reservoir results principally from the intrinsic stability of CD4+ T cell memory, increasing the potency of anti-HIV therapies may not be sufficient to eradicate HIV.
PMCID: PMC1599767  PMID: 17040122
23.  A Systems Immunology Approach to Plasmacytoid Dendritic Cell Function in Cytopathic Virus Infections 
PLoS Pathogens  2010;6(7):e1001017.
Plasmacytoid dendritic cell (pDC)-mediated protection against cytopathic virus infection involves various molecular, cellular, tissue-scale, and organism-scale events. In order to better understand such multiscale interactions, we have implemented a systems immunology approach focusing on the analysis of the structure, dynamics and operating principles of virus-host interactions which constrain the initial spread of the pathogen. Using high-resolution experimental data sets coming from the well-described mouse hepatitis virus (MHV) model, we first calibrated basic modules including MHV infection of its primary target cells, i.e. pDCs and macrophages (Mφs). These basic building blocks were used to generate and validate an integrative mathematical model for in vivo infection dynamics. Parameter estimation for the system indicated that on a per capita basis, one infected pDC secretes sufficient type I IFN to protect 103 to 104 Mφs from cytopathic viral infection. This extremely high protective capacity of pDCs secures the spleen's capability to function as a ‘sink’ for the virus produced in peripheral organs such as the liver. Furthermore, our results suggest that the pDC population in spleen ensures a robust protection against virus variants which substantially down-modulate IFN secretion. However, the ability of pDCs to protect against severe disease caused by virus variants exhibiting an enhanced liver tropism and higher replication rates appears to be rather limited. Taken together, this systems immunology analysis suggests that antiviral therapy against cytopathic viruses should primarily limit viral replication within peripheral target organs.
Author Summary
Human infections with highly virulent viruses, such as 1918 influenza or SARS-coronavirus, represent major threats to public health. The initial innate immune responses to such viruses have to restrict virus spread before the adaptive immune responses fully develop. Therefore, it is of fundamental practical importance to understand the robustness and fragility of the early protection against such virus infections mediated by the type I interferon (IFN) response. Because of the inherent complexity of the virus-host system, we have used mathematical modeling to predict the sensitivity of the kinetics and severity of infection to variations in virus and host parameters. Our results suggest that the spleen represents a robust sink system for systemic virus infection and that this system is able to cope with substantial variations in IFN secretion and virus production. However, the system is very fragile to only minor increases in the virus growth rate in peripheral tissues. Collectively, the mathematical approach described in this study allows us to identify the most robust virus and host parameters during early cytopathic virus infection and can serve as a paradigm for systems immunology analyses of multiscale virus-host interaction of many life-threatening cytopathic virus infections.
PMCID: PMC2908624  PMID: 20661432
24.  Impaired Hepatitis C Virus-Specific T Cell Responses and Recurrent Hepatitis C Virus in HIV Coinfection 
PLoS Medicine  2006;3(12):e492.
Hepatitis C virus (HCV)-specific T cell responses are critical for spontaneous resolution of HCV viremia. Here we examined the effect of a lymphotropic virus, HIV-1, on the ability of coinfected patients to maintain spontaneous control of HCV infection.
Methods and Findings
We measured T cell responsiveness by lymphoproliferation and interferon-γ ELISPOT in a large cohort of HCV-infected individuals with and without HIV infection. Among 47 HCV/HIV-1-coinfected individuals, spontaneous control of HCV was associated with more frequent HCV-specific lymphoproliferative (LP) responses (35%) compared to coinfected persons who exhibited chronic HCV viremia (7%, p = 0.016), but less frequent compared to HCV controllers who were not HIV infected (86%, p = 0.003). Preservation of HCV-specific LP responses in coinfected individuals was associated with a higher nadir CD4 count (r2 = 0.45, p < 0.001) and the presence and magnitude of the HCV-specific CD8+ T cell interferon-γ response (p = 0.0014). During long-term follow-up, recurrence of HCV viremia occurred in six of 25 coinfected individuals with prior control of HCV, but in 0 of 16 HIV-1-negative HCV controllers (p = 0.03, log rank test). In these six individuals with recurrent HCV viremia, the magnitude of HCV viremia following recurrence inversely correlated with the CD4 count at time of breakthrough (r = −0.94, p = 0.017).
These results indicate that HIV infection impairs the immune response to HCV—including in persons who have cleared HCV infection—and that HIV-1-infected individuals with spontaneous control of HCV remain at significant risk for a second episode of HCV viremia. These findings highlight the need for repeat viral RNA testing of apparent controllers of HCV infection in the setting of HIV-1 coinfection and provide a possible explanation for the higher rate of HCV persistence observed in this population.
HIV infection impairs the immune response to HCV. Even individuals who have cleared HCV infection remain at significant risk for a second episode of HCV viremia.
Editors' Summary
Because of shared transmission routes (contaminated needles, contaminated blood products, and, to a lesser extent, unprotected sex), a large proportion of HIV-infected individuals (estimates range between 25% and 33%) are also infected with the hepatitis C virus (HCV). In most but not all individuals infected with HCV, the virus infection is chronic and causes liver disease that can eventually lead to liver failure. Disease progress is slow; it often takes decades until infected individuals develop serious liver disease. In people infected with both HCV and HIV, however, liver disease caused by HCV often appears sooner and progresses faster. As highly active antiretroviral therapy (HAART) and prophylaxis of opportunistic infections increase the life span of persons living with HIV, HCV-related liver disease has become a major cause of hospital admissions and deaths among HIV-infected persons.
Why Was This Study Done?
A sizable minority of people who are infected with HCV manage to control the virus and never get liver disease, and scientists have found that these people somehow mounted a strong immune response against the hepatitis C virus. CD4+ T cells, the very immune cells that are infected and destroyed by HIV, play an important role in this immune response. The goal of the present study was to better understand how infection with HIV compromises the specific immune response to HCV and thereby the control of HCV disease progression.
What Did the Researchers Do and Find?
The researchers recruited four groups of patients, 94 in total, all of whom were infected with HCV. Two groups comprised patients who were infected with HIV as well as HCV, with either high or undetectable levels of HCV (30 patients in each group). The two other groups included patients not infected with HIV, either with high or undetectable levels of HCV (17 patients in each group). The researchers focused on the individuals who, despite coinfection with HIV, were able to control their HCV infection. They found that those individuals managed to maintain relatively high levels of CD4+ T cells that specifically recognize HCV. However, a quarter of these patients (six out of 25) failed to keep HCV levels down for the entire observation period of up to 2.5 years; their blood levels of HCV rose substantially, most likely due to recurrence of the previously suppressed virus (the researchers could not be certain that none of the patients had become infected again after a new exposure to HCV-contaminated blood, but there was no evidence that they had engaged in risky behavior). The rise of HCV levels in the blood of the relapsed patients coincided with a drop in overall CD4+ T cell numbers. Following relapse in these individuals, HCV did not return to undetectable levels during the study. During the same period none of the 16 HIV-uninfected people with controlled HCV infection experienced a recurrence of detectable HCV.
What Do These Findings Mean?
Despite the relatively small numbers of patients, these results suggest that recurrence of HCV after initial control of the virus is more likely in people who are coinfected with HIV, and that HCV control is lost when CD4+ T cell counts fall. This is one more reason to test all HIV-positive patients for HCV coinfection. Coinfected patients, even those who seem to be controlling HCV and would not automatically receive HCV treatment, should be regularly tested for a rise of HCV levels. In addition, maintaining CD4+ T cells at a high level might be particularly important for those patients, which means that doctors might consider starting HAART therapy earlier than is generally recommended for HIV-infected individuals. Additional studies are needed to support these recommendations, however, especially as this study did not follow the patients long enough to determine the consequences of the observed loss of control of HCV.
Additional Information.
Please access these Web sites via the online version of this summary at
AIDS Treatment Data Network factsheet on HIV/HCV coinfection
US CDC factsheet on HIV/HCV coinfection
American Liver Foundation, information on HIV and HCV
MedlinePlus pages on HCV
PMCID: PMC1705826  PMID: 17194190
25.  Estimating the In Vivo Killing Efficacy of Cytotoxic T Lymphocytes across Different Peptide-MHC Complex Densities 
PLoS Computational Biology  2015;11(5):e1004178.
Cytotoxic T lymphocytes (CTLs) are important agents in the control of intracellular pathogens, which specifically recognize and kill infected cells. Recently developed experimental methods allow the estimation of the CTL's efficacy in detecting and clearing infected host cells. One method, the in vivo killing assay, utilizes the adoptive transfer of antigen displaying target cells into the bloodstream of mice. Surprisingly, killing efficacies measured by this method are often much higher than estimates obtained by other methods based on, for instance, the dynamics of escape mutations. In this study, we investigated what fraction of this variation can be explained by differences in peptide loads employed in in vivo killing assays. We addressed this question in mice immunized with lymphocytic choriomeningitis virus (LCMV). We conducted in vivo killing assays varying the loads of the immunodominant epitope GP33 on target cells. Using a mathematical model, we determined the efficacy of effector and memory CTL, as well as CTL in chronically infected mice. We found that the killing efficacy is substantially reduced at lower peptide loads. For physiological peptide loads, our analysis predicts more than a factor 10 lower CTL efficacies than at maximum peptide loads. Assuming that the efficacy scales linearly with the frequency of CTL, a clear hierarchy emerges among the groups across all peptide antigen concentrations. The group of mice with chronic LCMV infections shows a consistently higher killing efficacy per CTL than the acutely infected mouse group, which in turn has a consistently larger efficacy than the memory mouse group. We conclude that CTL killing efficacy dependence on surface epitope frequencies can only partially explain the variation in in vivo killing efficacy estimates across experimental methods and viral systems, which vary about four orders of magnitude. In contrast, peptide load differences can explain at most two orders of magnitude.
Author Summary
The immune system reacts to the presence of a viral pathogen within the host by the elicitation of an immune response. This response is characterized by the activation and proliferation of specific cell types, which, for instance, produce neutralizing antibodies or kill cells infected by the virus. Cytotoxic T lymphocytes (CTLs) function as an important protecting element of the system by recognizing and clearing infected viral target cells. Surprisingly, estimates of the killing efficacy of CTLs vary about four orders of magnitude across experimental methods and viral systems. In some studies, CTL killing efficacies were estimated by employing pre-treated cells that mimick virus infected cells. In general, cells signal their infection by a pathogen to the immune system by presenting viral peptides on their cellular surface. For the experimentally pretreated cells, these peptides were artificially loaded onto the surface at very high densities. In this paper, we study to what extent the variation in peptide densities can explain the variation found in killing efficacy estimates across methods and viral systems. We found that peptide densities explain only up to two orders of magnitude in killing efficacy variation. The remaining variation must originate from other sources, which might be specific to the viral study system.
PMCID: PMC4416789  PMID: 25933039

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