Alison L. Hill

Rapid seeding of the viral reservoir prior to SIV viraemia in rhesus monkeys

The viral reservoir represents a critical challenge for human immunodeficiency virus type 1 (HIV-1) eradication strategies. However, it remains unclear when and where the viral reservoir is seeded during acute infection and the extent to which it is susceptible to early antiretroviral therapy (ART). Here we show that the viral reservoir is seeded rapidly after mucosal simian immunodeficiency virus (SIV) infection of rhesus monkeys and before systemic viraemia. We initiated suppressive ART in groups of monkeys on days 3, 7, 10 and 14 after intrarectal SIVMAC251 infection. Treatment with ART on day 3 blocked the emergence of viral RNA and proviral DNA in peripheral blood and also substantially reduced levels of proviral DNA in lymph nodes and gastrointestinal mucosa as compared with treatment at later time points. In addition, treatment on day 3 abrogated the induction of SIV-specific humoral and cellular immune responses. Nevertheless, after discontinuation of ART following 24 weeks of fully suppressive therapy, virus rebounded in all animals, although the monkeys that were treated on day 3 exhibited a delayed viral rebound as compared with those treated on days 7, 10 and 14. The time to viral rebound correlated with total viraemia during acute infection and with proviral DNA at the time of ART discontinuation. These data demonstrate that the viral reservoir is seeded rapidly after intrarectal SIV infection of rhesus monkeys, during the ‘eclipse’ phase, and before detectable viraemia. This strikingly early seeding of the refractory viral reservoir raises important new challenges for HIV-1 eradication strategies.

Ex vivo analysis identifies effective HIV-1 latency–reversing drug combinations

Reversal of HIV-1 latency by small molecules is a potential cure strategy. This approach will likely require effective drug combinations to achieve high levels of latency reversal. Using resting CD4+ T cells (rCD4s) from infected individuals, we developed an experimental and theoretical framework to identify effective latency-reversing agent (LRA) combinations. Utilizing ex vivo assays for intracellular HIV-1 mRNA and virion production, we compared 2-drug combinations of leading candidate LRAs and identified multiple combinations that effectively reverse latency. We showed that protein kinase C agonists in combination with bromodomain inhibitor JQ1 or histone deacetylase inhibitors robustly induce HIV-1 transcription and virus production when directly compared with maximum reactivation by T cell activation. Using the Bliss independence model to quantitate combined drug effects, we demonstrated that these combinations synergize to induce HIV-1 transcription. This robust latency reversal occurred without release of proinflammatory cytokines by rCD4s. To extend the clinical utility of our findings, we applied a mathematical model that estimates in vivo changes in plasma HIV-1 RNA from ex vivo measurements of virus production. Our study reconciles diverse findings from previous studies, establishes a quantitative experimental approach to evaluate combinatorial LRA efficacy, and presents a model to predict in vivo responses to LRAs.

Predicting the outcomes of treatment to eradicate the latent reservoir for HIV-1

Significance HIV infection cannot be cured by current antiretroviral drugs, due to the presence of long-lived latently infected cells. New antilatency drugs are being tested in clinical trials, but major unknowns remain. It is unclear how much latent virus must be eliminated for a cure, which remains difficult to answer empirically due to few case studies and limited sensitivity of viral reservoir assays. In this paper, we introduce a mathematical model of HIV dynamics to calculate the likelihood and timing of viral rebound following antilatency treatment. We derive predictions for the required efficacy of antilatency drugs, and demonstrate that rebound times may be highly variable and occur after years of remission. These results will aid in designing and interpreting HIV cure studies. Massive research efforts are now underway to develop a cure for HIV infection, allowing patients to discontinue lifelong combination antiretroviral therapy (ART). New latency-reversing agents (LRAs) may be able to purge the persistent reservoir of latent virus in resting memory CD4+ T cells, but the degree of reservoir reduction needed for cure remains unknown. Here we use a stochastic model of infection dynamics to estimate the efficacy of LRA needed to prevent viral rebound after ART interruption. We incorporate clinical data to estimate population-level parameter distributions and outcomes. Our findings suggest that ∼2,000-fold reductions are required to permit a majority of patients to interrupt ART for 1 y without rebound and that rebound may occur suddenly after multiple years. Greater than 10,000-fold reductions may be required to prevent rebound altogether. Our results predict large variation in rebound times following LRA therapy, which will complicate clinical management. This model provides benchmarks for moving LRAs from the laboratory to the clinic and can aid in the design and interpretation of clinical trials. These results also apply to other interventions to reduce the latent reservoir and can explain the observed return of viremia after months of apparent cure in recent bone marrow transplant recipients and an immediately-treated neonate.

Emotions as infectious diseases in a large social network: the SISa model

Human populations are arranged in social networks that determine interactions and influence the spread of diseases, behaviours and ideas. We evaluate the spread of long-term emotional states across a social network. We introduce a novel form of the classical susceptible–infected–susceptible disease model which includes the possibility for ‘spontaneous’ (or ‘automatic’) infection, in addition to disease transmission (the SISa model). Using this framework and data from the Framingham Heart Study, we provide formal evidence that positive and negative emotional states behave like infectious diseases spreading across social networks over long periods of time. The probability of becoming content is increased by 0.02 per year for each content contact, and the probability of becoming discontent is increased by 0.04 per year per discontent contact. Our mathematical formalism allows us to derive various quantities from the data, such as the average lifetime of a contentment ‘infection’ (10 years) or discontentment ‘infection’ (5 years). Our results give insight into the transmissive nature of positive and negative emotional states. Determining to what extent particular emotions or behaviours are infectious is a promising direction for further research with important implications for social science, epidemiology and health policy. Our model provides a theoretical framework for studying the interpersonal spread of any state that may also arise spontaneously, such as emotions, behaviours, health states, ideas or diseases with reservoirs.

Antiretroviral dynamics determines HIV evolution and predicts therapy outcome

Despite the high inhibition of viral replication achieved by current anti-HIV drugs, many patients fail treatment, often with emergence of drug-resistant virus. Clinical observations show that the relationship between adherence and likelihood of resistance differs dramatically among drug classes. We developed a mathematical model that explains these observations and predicts treatment outcomes. Our model incorporates drug properties, fitness differences between susceptible and resistant strains, mutations and adherence. We show that antiviral activity falls quickly for drugs with sharp dose-response curves and short half-lives, such as boosted protease inhibitors, limiting the time during which resistance can be selected for. We find that poor adherence to such drugs causes treatment failure via growth of susceptible virus, explaining puzzling clinical observations. Furthermore, our model predicts that certain single-pill combination therapies can prevent resistance regardless of patient adherence. Our approach represents a first step for simulating clinical trials of untested anti-HIV regimens and may help in the selection of new drug regimens for investigation.

Infectious Disease Modeling of Social Contagion in Networks

Many behavioral phenomena have been found to spread interpersonally through social networks, in a manner similar to infectious diseases. An important difference between social contagion and traditional infectious diseases, however, is that behavioral phenomena can be acquired by non-social mechanisms as well as through social transmission. We introduce a novel theoretical framework for studying these phenomena (the SISa model) by adapting a classic disease model to include the possibility for ‘automatic’ (or ‘spontaneous’) non-social infection. We provide an example of the use of this framework by examining the spread of obesity in the Framingham Heart Study Network. The interaction assumptions of the model are validated using longitudinal network transmission data. We find that the current rate of becoming obese is 2 per year and increases by 0.5 percentage points for each obese social contact. The rate of recovering from obesity is 4 per year, and does not depend on the number of non-obese contacts. The model predicts a long-term obesity prevalence of approximately 42, and can be used to evaluate the effect of different interventions on steady-state obesity. Model predictions quantitatively reproduce the actual historical time course for the prevalence of obesity. We find that since the 1970s, the rate of recovery from obesity has remained relatively constant, while the rates of both spontaneous infection and transmission have steadily increased over time. This suggests that the obesity epidemic may be driven by increasing rates of becoming obese, both spontaneously and transmissively, rather than by decreasing rates of losing weight. A key feature of the SISa model is its ability to characterize the relative importance of social transmission by quantitatively comparing rates of spontaneous versus contagious infection. It provides a theoretical framework for studying the interpersonal spread of any state that may also arise spontaneously, such as emotions, behaviors, health states, ideas or diseases with reservoirs.

Silicon Nanoparticles as Hyperpolarized Magnetic Resonance Imaging Agents

Silicon nanoparticles are experimentally investigated as a potential hyperpolarized, targetable MRI imaging agent. Nuclear T_1 times at room temperature for a variety of Si nanoparticles are found to be remarkably long (10^2 to 10^4 s) - roughly consistent with predictions of a core-shell diffusion model - allowing them to be transported, administered and imaged on practical time scales without significant loss of polarization. We also report surface functionalization of Si nanoparticles, comparable to approaches used in other biologically targeted nanoparticle systems.Magnetic resonance imaging of hyperpolarized nuclei provides high image contrast with little or no background signal. To date, in vivo applications of prehyperpolarized materials have been limited by relatively short nuclear spin relaxation times. Here, we investigate silicon nanoparticles as a new type of hyperpolarized magnetic resonance imaging agent. Nuclear spin relaxation times for a variety of Si nanoparticles are found to be remarkably long, ranging from many minutes to hours at room temperature, allowing hyperpolarized nanoparticles to be transported, administered, and imaged on practical time scales. Additionally, we demonstrate that Si nanoparticles can be surface functionalized using techniques common to other biologically targeted nanoparticle systems. These results suggest that Si nanoparticles can be used as a targetable, hyperpolarized magnetic resonance imaging agent with a large range of potential applications.

Ad26/MVA therapeutic vaccination with TLR7 stimulation in SIV-infected rhesus monkeys

The development of immunologic interventions that can target the viral reservoir in HIV-1-infected individuals is a major goal of HIV-1 research. However, little evidence exists that the viral reservoir can be sufficiently targeted to improve virologic control following discontinuation of antiretroviral therapy. Here we show that therapeutic vaccination with Ad26/MVA (recombinant adenovirus serotype 26 (Ad26) prime, modified vaccinia Ankara (MVA) boost) and stimulation of TLR7 (Toll-like receptor 7) improves virologic control and delays viral rebound following discontinuation of antiretroviral therapy in SIV-infected rhesus monkeys that began antiretroviral therapy during acute infection. Therapeutic vaccination with Ad26/MVA resulted in a marked increase in the magnitude and breadth of SIV-specific cellular immune responses in virologically suppressed, SIV-infected monkeys. TLR7 agonist administration led to innate immune stimulation and cellular immune activation. The combination of Ad26/MVA vaccination and TLR7 stimulation resulted in decreased levels of viral DNA in lymph nodes and peripheral blood, and improved virologic control and delayed viral rebound following discontinuation of antiretroviral therapy. The breadth of cellular immune responses correlated inversely with set point viral loads and correlated directly with time to viral rebound. These data demonstrate the potential of therapeutic vaccination combined with innate immune stimulation as a strategy aimed at a functional cure for HIV-1 infection.

Designing and Interpreting Limiting Dilution Assays: General Principles and Applications to the Latent Reservoir for Human Immunodeficiency Virus-1.

Limiting dilution assays are widely used in infectious disease research. These assays are crucial for current HIV-1 cure research in particular. Here we offer new tools to help investigators design and analyze dilution assays based on their specific research needs.

Imperfect drug penetration leads to spatial monotherapy and rapid evolution of multidrug resistance

Significance The evolution of drug resistance is a major health threat. In chronic infections with rapidly mutating pathogens—including HIV, tuberculosis, and hepatitis B and C viruses—multidrug resistance can cause even aggressive combination drug treatment to fail. Oftentimes, individual drugs within a combination do not penetrate equally to all infected regions of the body. Here we present a mathematical model suggesting that this imperfect penetration can dramatically increase the chance of treatment failure by creating regions where only one drug from a combination reaches a therapeutic concentration. The resulting single-drug compartments allow the pathogen to evolve resistance to each drug sequentially, rapidly causing multidrug resistance. More broadly, our model provides a quantitative framework for reasoning about trade-offs between aggressive and moderate drug therapies. Infections with rapidly evolving pathogens are often treated using combinations of drugs with different mechanisms of action. One of the major goal of combination therapy is to reduce the risk of drug resistance emerging during a patient’s treatment. Although this strategy generally has significant benefits over monotherapy, it may also select for multidrug-resistant strains, particularly during long-term treatment for chronic infections. Infections with these strains present an important clinical and public health problem. Complicating this issue, for many antimicrobial treatment regimes, individual drugs have imperfect penetration throughout the body, so there may be regions where only one drug reaches an effective concentration. Here we propose that mismatched drug coverage can greatly speed up the evolution of multidrug resistance by allowing mutations to accumulate in a stepwise fashion. We develop a mathematical model of within-host pathogen evolution under spatially heterogeneous drug coverage and demonstrate that even very small single-drug compartments lead to dramatically higher resistance risk. We find that it is often better to use drug combinations with matched penetration profiles, although there may be a trade-off between preventing eventual treatment failure due to resistance in this way and temporarily reducing pathogen levels systemically. Our results show that drugs with the most extensive distribution are likely to be the most vulnerable to resistance. We conclude that optimal combination treatments should be designed to prevent this spatial effective monotherapy. These results are widely applicable to diverse microbial infections including viruses, bacteria, and parasites.