Rustom Antia

Lineage relationship and protective immunity of memory CD8 T cell subsets

Memory CD8 T cells can be divided into two subsets, central (TCM) and effector (TEM), but their lineage relationships and their ability to persist and confer protective immunity are not well understood. Our results show that TCM have a greater capacity than TEM to persist in vivo and are more efficient in mediating protective immunity because of their increased proliferative potential. We also demonstrate that, following antigen clearance, TEM convert to TCM and that the duration of this differentiation is programmed within the first week after immunization. We propose that TCM and TEM do not necessarily represent distinct subsets, but are part of a continuum in a linear naive → effector → TEM → TCM differentiation pathway.

Humoral immunity due to long-lived plasma cells

Conventional models suggest that long-term antibody responses are maintained by the continuous differentiation of memory B cells into antibody-secreting plasma cells. This is based on the notion that plasma cells are short-lived and need to be continually replenished by memory B cells. We examined the issue of plasma cell longevity by following the persistence of LCMV-specific antibody and plasma cell numbers after in vivo depletion of memory B cells and by adoptive transfer of virus-specific plasma cells into naive mice. The results show that a substantial fraction of plasma cells can survive and continue to secrete antibody for extended periods of time (>1 year) in the absence of any detectable memory B cells. This study documents the existence of long-lived plasma cells and demonstrates a new mechanism by which humoral immunity is maintained.

Interleukin 15 Is Required for Proliferative Renewal of Virus-specific Memory CD8 T Cells

The generation and efficient maintenance of antigen-specific memory T cells is essential for long-lasting immunological protection. In this study, we examined the role of interleukin (IL)-15 in the generation and maintenance of virus-specific memory CD8 T cells using mice deficient in either IL-15 or the IL-15 receptor α chain. Both cytokine- and receptor-deficient mice made potent primary CD8 T cell responses to infection with lymphocytic choriomeningitis virus (LCMV), effectively cleared the virus and generated a pool of antigen-specific memory CD8 T cells that were phenotypically and functionally similar to memory CD8 T cells present in IL-15+/+ mice. However, longitudinal analysis revealed a slow attrition of virus-specific memory CD8 T cells in the absence of IL-15 signals.This loss of CD8 T cells was due to a severe defect in the proliferative renewal of antigen-specific memory CD8 T cells in IL-15−/− mice. Taken together, these results show that IL-15 is not essential for the generation of memory CD8 T cells, but is required for homeostatic proliferation to maintain populations of memory cells over long periods of time.

Emerging pathogens: the epidemiology and evolution of species jumps

Novel pathogens continue to emerge in human, domestic animal, wildlife and plant populations, yet the population dynamics of this kind of biological invasion remain poorly understood. Here, we consider the epidemiological and evolutionary processes underlying the initial introduction and subsequent spread of a pathogen in a new host population, with special reference to pathogens that originate by jumping from one host species to another. We conclude that, although pathogen emergence is inherently unpredictable, emerging pathogens tend to share some common traits, and that directly transmitted RNA viruses might be the pathogens that are most likely to jump between host species.

The role of evolution in the emergence of infectious diseases

It is unclear when, where and how novel pathogens such as human immunodeficiency virus (HIV), monkeypox and severe acute respiratory syndrome (SARS) will cross the barriers that separate their natural reservoirs from human populations and ignite the epidemic spread of novel infectious diseases. New pathogens are believed to emerge from animal reservoirs when ecological changes increase the pathogens opportunities to enter the human population and to generate subsequent human-to-human transmission. Effective human-to-human transmission requires that the pathogens basic reproductive number, R0, should exceed one, where R0 is the average number of secondary infections arising from one infected individual in a completely susceptible population. However, an increase in R0, even when insufficient to generate an epidemic, nonetheless increases the number of subsequently infected individuals. Here we show that, as a consequence of this, the probability of pathogen evolution to R0 > 1 and subsequent disease emergence can increase markedly.

Recruitment Times, Proliferation, and Apoptosis Rates during the CD8+ T-Cell Response to Lymphocytic Choriomeningitis Virus

ABSTRACT The specific CD8+ T-cell response during acute lymphocytic choriomeningitis virus (LCMV) infection of mice is characterized by a rapid proliferation phase, followed by a rapid death phase and long-term memory. In BALB/c mice the immunodominant and subdominant CD8+ responses are directed against the NP118 and GP283 epitopes. These responses differ mainly in the magnitude of the epitope-specific CD8+ T-cell expansion. Using mathematical models together with a nonlinear parameter estimation procedure, we estimate the parameters describing the rates of change during the three phases and thereby establish the differences between the responses to the two epitopes. We find that CD8+ cell proliferation begins 1 to 2 days after infection and occurs at an average rate of 3 day−1, reaching the maximum population size between days 5 and 6 after immunization. The 10-fold difference in expansion to the NP118 and GP283 epitopes can be accounted for in our model by a 3.5-fold difference in the antigen concentration of these epitopes at which T-cell stimulation is half-maximal. As a consequence of this 3.5-fold difference in the epitope concentration needed for T-cell stimulation, the rates of activation and proliferation of T cells specific for the two epitopes differ during the response and in combination can account for the large difference in the magnitude of the response. After the peak, during the death phase, the population declines at a rate of 0.5 day−1, i.e., cells have an average life time of 2 days. The model accounts for a memory cell population of 5% of the peak population size by a reversal to memory of 1 to 2% of the activated cells per day during the death phase.

Epidemiological models for the spread of anti-malarial resistance

BackgroundThe spread of drug resistance is making malaria control increasingly difficult. Mathematical models for the transmission dynamics of drug sensitive and resistant strains can be a useful tool to help to understand the factors that influence the spread of drug resistance, and they can therefore help in the design of rational strategies for the control of drug resistance.MethodsWe present an epidemiological framework to investigate the spread of anti-malarial resistance. Several mathematical models, based on the familiar Macdonald-Ross model of malaria transmission, enable us to examine the processes and parameters that are critical in determining the spread of resistance.ResultsIn our simplest model, resistance does not spread if the fraction of infected individuals treated is less than a threshold value; if drug treatment exceeds this threshold, resistance will eventually become fixed in the population. The threshold value is determined only by the rates of infection and the infectious periods of resistant and sensitive parasites in untreated and treated hosts, whereas the intensity of transmission has no influence on the threshold value. In more complex models, where hosts can be infected by multiple parasite strains or where treatment varies spatially, resistance is generally not fixed, but rather some level of sensitivity is often maintained in the population.ConclusionsThe models developed in this paper are a first step in understanding the epidemiology of anti-malarial resistance and evaluating strategies to reduce the spread of resistance. However, specific recommendations for the management of resistance need to wait until we have more data on the critical parameters underlying the spread of resistance: drug use, spatial variability of treatment and parasite migration among areas, and perhaps most importantly, cost of resistance.

WITHIN-HOST POPULATION DYNAMICS AND THE EVOLUTION OF MICROPARASITES IN A HETEROGENEOUS HOST POPULATION

Abstract Why do parasites harm their hosts? The general understanding is that if the transmission rate and virulence of a parasite are linked, then the parasite must harm its host to maximize its transmission. The exact nature of such trade‐offs remains largely unclear, but for vertebrate hosts it probably involves interactions between a microparasite and the host immune system. Previous results have suggested that in a homogeneous host population in the absence of super‐ or coinfection, within‐host dynamics lead to selection of the parasite with an intermediate growth rate that is just being controlled by the immune system before it kills the host (Antia et al. 1994). In this paper, we examine how this result changes when heterogeneity is introduced to the host population. We incorporate the simplest form of heterogeneity–random heterogeneity in the parameters describing the size of the initial parasite inoculum, the immune response of the host, and the lethal density at which the parasite kills the host. We find that the general conclusion of the previous model holds: parasites evolve some intermediate growth rate. However, in contrast with the generally accepted view, we find that virulence (measured by the case mortality or the rate of parasite‐induced host mortality) increases with heterogeneity. Finally, we link the within‐host and between‐host dynamics of parasites. We show how the parameters for epidemiological spread of the disease can be estimated from the within‐host dynamics, and in doing so examine the way in which trade‐offs between these epidemiological parameters arise as a consequence of the interaction of the parasite and the immune response of the host.

Neuraminidase Inhibitor Resistance in Influenza: Assessing the Danger of Its Generation and Spread

Neuraminidase Inhibitors (NI) are currently the most effective drugs against influenza. Recent cases of NI resistance are a cause for concern. To assess the danger of NI resistance, a number of studies have reported the fraction of treated patients from which resistant strains could be isolated. Unfortunately, those results strongly depend on the details of the experimental protocol. Additionally, knowing the fraction of patients harboring resistance is not too useful by itself. Instead, we want to know how likely it is that an infected patient can generate a resistant infection in a secondary host, and how likely it is that the resistant strain subsequently spreads. While estimates for these parameters can often be obtained from epidemiological data, such data is lacking for NI resistance in influenza. Here, we use an approach that does not rely on epidemiological data. Instead, we combine data from influenza infections of human volunteers with a mathematical framework that allows estimation of the parameters that govern the initial generation and subsequent spread of resistance. We show how these parameters are influenced by changes in drug efficacy, timing of treatment, fitness of the resistant strain, and details of virus and immune system dynamics. Our study provides estimates for parameters that can be directly used in mathematical and computational models to study how NI usage might lead to the emergence and spread of resistance in the population. We find that the initial generation of resistant cases is most likely lower than the fraction of resistant cases reported. However, we also show that the results depend strongly on the details of the within-host dynamics of influenza infections, and most importantly, the role the immune system plays. Better knowledge of the quantitative dynamics of the immune response during influenza infections will be crucial to further improve the results.

Towards a quantitative understanding of the within-host dynamics of influenza A infections.

Although the influenza A virus has been extensively studied, a quantitative understanding of the infection dynamics is still lacking. To make progress in this direction, we designed several mathematical models and compared them with data from influenza A infections of mice. We find that the immune response (IR) plays an important part in the infection dynamics. Both an innate and an adaptive IR are required to provide adequate explanation of the data. In contrast, regrowth of epithelial cells did not seem to be an important mechanism on the time scale of the infection. We also find that different model variants for both innate and adaptive responses fit the data well, indicating the need for additional data to allow further model discrimination.