Publications

HIV-1 accumulates mutations in and around reactive epitopes to escape recognition and killing by CD8+ T cells. Measurements of HIV-1 time to escape should therefore provide information on which parameters are most important for T cell-mediated in vivo control of HIV-1. Primary HIV-1-specific T cell responses were fully mapped in 17 individuals, and the time to virus escape, which ranged from days to years, was measured for each epitope. While higher magnitude of an individual T cell response was associated with more rapid escape, the most significant T cell measure was its relative immunodominance measured in acute infection. This identified subject-level or "vertical" immunodominance as the primary determinant of in vivo CD8+ T cell pressure in HIV-1 infection. Conversely, escape was slowed significantly by lower population variability, or entropy, of the epitope targeted. Immunodominance and epitope entropy combined to explain half of all the variability in time to escape. These data explain how CD8+ T cells can exert significant and sustained HIV-1 pressure even when escape is very slow and that within an individual, the impacts of other T cell factors on HIV-1 escape should be considered in the context of immunodominance.

Human immunodeficiency virus (HIV-1 or simply HIV) induces a persistent infection, which in the absence of treatment leads to AIDS and death in almost all infected individuals. HIV infection elicits a vigorous immune response starting about 2-3 weeks post infection that can lower the amount of virus in the body, but which cannot eradicate the virus. How HIV establishes a chronic infection in the face of a strong immune response remains poorly understood. It has been shown that HIV is able to rapidly change its proteins via mutation to evade recognition by virus-specific cytotoxic T lymphocytes (CTLs). Typically, an HIV-infected patient will generate 4-12 CTL responses specific for parts of viral proteins called epitopes. Such CTL responses lead to strong selective pressure to change the viral sequences encoding these epitopes so as to avoid CTL recognition. Indeed, the viral population "escapes" from about half of the CTL responses by mutation in the first year. Here we review experimental data on HIV evolution in response to CTL pressure, mathematical models developed to explain this evolution, and highlight problems associated with the data and previous modeling efforts. We show that estimates of the strength of the epitope-specific CTL response depend on the method used to fit models to experimental data and on the assumptions made regarding how mutants are generated during infection. We illustrate that allowing CTL responses to decay over time may improve the fit to experimental data and provides higher estimates of the killing efficacy of HIV-specific CTLs. We also propose a novel method for simultaneously estimating the killing efficacy of multiple CTL populations specific for different epitopes of HIV using stochastic simulations. Lastly, we show that current estimates of the efficacy at which HIV-specific CTLs clear virus-infected cells can be improved by more frequent sampling of viral sequences and by combining data on sequence evolution with experimentally measured CTL dynamics.

Vertebrates are constantly exposed to pathogens, and the adaptive immunity has most likely evolved to control and clear such infectious agents. CD4(+) T cells are the major players in the adaptive immune response to pathogens. Following recognition of pathogen-derived antigens naïve CD4(+) T cells differentiate into effectors which then control pathogen replication either directly by killing pathogen-infected cells or by assisting with generation of cytotoxic T lymphocytes (CTLs) or pathogen-specific antibodies. Pathogen-specific effector CD4(+) T cells are highly heterogeneous in terms of cytokines they produce. Three major subtypes of effector CD4(+) T cells have been identified: T-helper 1 (Th1) cells producing IFN-γ and TNF-α, Th2 cells producing IL-4 and IL-10, and Th17 cells producing IL-17. How this heterogeneity is maintained and what regulates changes in effector T cell composition during chronic infections remains poorly understood. In this review we discuss recent advances in our understanding of CD4(+) T cell differentiation in response to microbial infections. We propose that a change in the phenotype of pathogen-specific effector CD4(+) T cells during chronic infections, for example, from Th1 to Th2 response as observed in Mycobactrium avium ssp. paratuberculosis (MAP) infection of ruminants, can be achieved by conversion of T cells from one effector subset to another (cellular plasticity) or due to differences in kinetics (differentiation, proliferation, death) of different effector T cell subsets (population plasticity). We also shortly review mathematical models aimed at describing CD4(+) T cell differentiation and outline areas for future experimental and theoretical research.

Bromodeoxyuridine (BrdU) is widely used in immunology to detect cell division, and several mathematical models have been proposed to estimate proliferation and death rates of lymphocytes from BrdU labelling and de-labelling curves. One problem in interpreting BrdU data is explaining the de-labelling curves. Because shortly after label withdrawal, BrdU+ cells are expected to divide into BrdU+ daughter cells, one would expect a flat down-slope. As for many cell types, the fraction of BrdU+ cells decreases during de-labelling, previous mathematical models had to make debatable assumptions to be able to account for the data. We develop a mechanistic model tracking the number of divisions that each cell has undergone in the presence and absence of BrdU, and allow cells to accumulate and dilute their BrdU content. From the same mechanistic model, one can naturally derive expressions for the mean BrdU content (MBC) of all cells, or the MBC of the BrdU+ subset, which is related to the mean fluorescence intensity of BrdU that can be measured in experiments. The model is extended to include subpopulations with different rates of division and death (i.e. kinetic heterogeneity). We fit the extended model to previously published BrdU data from memory T lymphocytes in simian immunodeficiency virus-infected and uninfected macaques, and find that the model describes the data with at least the same quality as previous models. Because the same model predicts a modest decline in the MBC of BrdU+ cells, which is consistent with experimental observations, BrdU dilution seems a natural explanation for the observed down-slopes in self-renewing populations.

Viruses in mammals are constantly faced with the problem of elimination by the host immunity. Cytotoxic T lymphocyte (CTL) responses are thought to play a major role in the control and clearance of several viral infections in mice and humans. It is therefore expected that over evolutionary time, viruses would be forced to evolve to avoid recognition by CTLs. Indeed, a number of studies have documented the accumulation of viral variants with escape mutations. These mutations allow viruses to hide from CTL responses common in the host population. CTLs recognize viruses by short protein sequences, named epitopes, derived from viral proteins. The efficiency of viral recognition by epitope-specific CTL responses depends on the expression pattern of the proteins carrying these epitopes, and the total amount of that protein (and thus epitopes) in the cell. When a virus replicates in a cell, some viral genes are expressed early in the life cycle of the virus, while other proteins are expressed late. For example, HIV infected cells first express Rev and Tat proteins, and the Gag proteins are expressed late. Here we propose a dynamical model of the viral life cycle to study how expression level of early vs. late genes may affect viral dynamics within the host and virus transmission over the course of infection. We find that for acute and chronic viral infections lower expression of early genes than that of the late genes is expected to give selective advantage and higher transmission to viruses.

Single genome sequencing of early HIV-1 genomes provides a sensitive, dynamic assessment of virus evolution and insight into the earliest anti-viral immune responses in vivo. By using this approach, together with deep sequencing, site-directed mutagenesis, antibody adsorptions and virus-entry assays, we found evidence in three subjects of neutralizing antibody (Nab) responses as early as 2 weeks post-seroconversion, with Nab titers as low as 1∶20 to 1∶50 (IC(50)) selecting for virus escape. In each of the subjects, Nabs targeted different regions of the HIV-1 envelope (Env) in a strain-specific, conformationally sensitive manner. In subject CH40, virus escape was first mediated by mutations in the V1 region of the Env, followed by V3. HIV-1 specific monoclonal antibodies from this subject mapped to an immunodominant region at the base of V3 and exhibited neutralizing patterns indistinguishable from polyclonal antibody responses, indicating V1-V3 interactions within the Env trimer. In subject CH77, escape mutations mapped to the V2 region of Env, several of which selected for alterations of glycosylation. And in subject CH58, escape mutations mapped to the Env outer domain. In all three subjects, initial Nab recognition was followed by sequential rounds of virus escape and Nab elicitation, with Nab escape variants exhibiting variable costs to replication fitness. Although delayed in comparison with autologous CD8 T-cell responses, our findings show that Nabs appear earlier in HIV-1 infection than previously recognized, target diverse sites on HIV-1 Env, and impede virus replication at surprisingly low titers. The unexpected in vivo sensitivity of early transmitted/founder virus to Nabs raises the possibility that similarly low concentrations of vaccine-induced Nabs could impair virus acquisition in natural HIV-1 transmission, where the risk of infection is low and the number of viruses responsible for transmission and productive clinical infection is typically one.

BACKGROUND: Effector CD4 T cells represent a key component of the host's anti-tuberculosis immune defense. Successful differentiation and functioning of effector lymphocytes protects the host against severe M. tuberculosis (Mtb) infection. On the other hand, effector T cell differentiation depends on disease severity/activity, as T cell responses are driven by antigenic and inflammatory stimuli released during infection. Thus, tuberculosis (TB) progression and the degree of effector CD4 T cell differentiation are interrelated, but the relationships are complex and not well understood. We have analyzed an association between the degree of Mtb-specific CD4 T cell differentiation and severity/activity of pulmonary TB infection.

METHODOLOGY/PRINCIPAL FINDINGS: The degree of CD4 T cell differentiation was assessed by measuring the percentages of highly differentiated CD27(low) cells within a population of Mtb- specific CD4 T lymphocytes ("CD27(low)IFN-γ(+)" cells). The percentages of CD27(low)IFN-γ+ cells were low in healthy donors (median, 33.1%) and TB contacts (21.8%) but increased in TB patients (47.3%, p<0.0005). Within the group of patients, the percentages of CD27(low)IFN-γ(+) cells were uniformly high in the lungs (>76%), but varied in blood (12-92%). The major correlate for the accumulation of CD27(low)IFN-γ(+) cells in blood was lung destruction (r = 0.65, p = 2.7 × 10(-7)). A cutoff of 47% of CD27(low)IFN-γ(+) cells discriminated patients with high and low degree of lung destruction (sensitivity 89%, specificity 74%); a decline in CD27(low)IFN-γ(+)cells following TB therapy correlated with repair and/or reduction of lung destruction (p<0.01).

CONCLUSIONS: Highly differentiated CD27(low) Mtb-specific (CD27(low)IFN-γ(+)) CD4 T cells accumulate in the lungs and circulate in the blood of patients with active pulmonary TB. Accumulation of CD27(low)IFN-γ(+) cells in the blood is associated with lung destruction. The findings indicate that there is no deficiency in CD4 T cell differentiation during TB; evaluation of CD27(low)IFN-γ(+) cells provides a valuable means to assess TB activity, lung destruction, and tissue repair following TB therapy.

HIV infection is characterized by a gradual deterioration of immune function, mainly in the CD4 compartment. To better understand the dynamics of HIV-specific T cells, we analyzed the kinetics and polyfunctional profiles of Gag-specific CD4(+) and CD8(+) T cell responses in 12 subtype C-infected individuals with different disease-progression profiles, ranging from acute to chronic HIV infection. The frequencies of Gag-responsive CD4(+) and CD8(+) T cells showed distinct temporal kinetics. The peak frequency of Gag-responsive IFN-γ(+)CD4(+) T cells was observed at a median of 28 d (interquartile range: 21-81 d) post-Fiebig I/II staging, whereas Gag-specific IFN-γ(+)CD8(+) T cell responses peaked at a median of 253 d (interquartile range: 136-401 d) and showed a significant biphasic expansion. The proportion of TNF-α-expressing cells within the IFN-γ(+)CD4(+) T cell population increased (p = 0.001) over time, whereas TNF-α-expressing cells within IFN-γ(+)CD8(+) T cells declined (p = 0.005). Both Gag-responsive CD4(+) and CD8(+) T cells showed decreased Ki67 expression within the first 120 d post-Fiebig I/II staging. Prior to the disappearance of Gag-responsive Ki67(+)CD4(+) T cells, these cells positively correlated (p = 0.00038) with viremia, indicating that early Gag-responsive CD4 events are shaped by viral burden. No such associations were observed in the Gag-specific CD8(+) T cell compartment. Overall, these observations indicated that circulating Gag-responsive CD4(+) and CD8(+) T cell frequencies and functions are not synchronous, and properties change rapidly at different tempos during early HIV infection.

It has been difficult to correlate the quality of CD8 T cell responses with protection against viral infections. To investigate the relationship between efficacy and magnitude of T cell responses, we quantify the rate at which individual CD8 effector and memory T cells kill target cells in the mouse spleen. Using mathematical modeling, we analyze recent data on the loss of target cells pulsed with three different peptides from the mouse lymphocytic choriomeningitis virus (LCMV) in mouse spleens with varying numbers of epitope-specific CD8 T cells. We find that the killing of targets follows the law of mass-action, i.e., the death rate of individual target cells remains proportional to the frequency (or the total number) of specific CD8 T cells in the spleen despite the fact that effector cell densities and effector to target ratios vary about a 1000-fold. The killing rate of LCMV-specific CD8 T cells is largely independent of T cell specificity and differentiation stage. Our results thus allow one to calculate the critical T cell concentration at which growth of a virus with a given replication rate can be prevented from the start of infection by memory CD8 T cell response.

HIV-1 often evades cytotoxic T cell (CTL) responses by generating variants that are not recognized by CTLs. We used single-genome amplification and sequencing of complete HIV genomes to identify longitudinal changes in the transmitted/founder virus from the establishment of infection to the viral set point at 1 year after the infection. We found that the rate of viral escape from CTL responses in a given patient decreases dramatically from acute infection to the viral set point. Using a novel mathematical model that tracks the dynamics of viral escape at multiple epitopes, we show that a number of factors could potentially contribute to a slower escape in the chronic phase of infection, such as a decreased magnitude of epitope-specific CTL responses, an increased fitness cost of escape mutations, or an increased diversity of the CTL response. In the model, an increase in the number of epitope-specific CTL responses can reduce the rate of viral escape from a given epitope-specific CTL response, particularly if CD8+ T cells compete for killing of infected cells or control virus replication nonlytically. Our mathematical framework of viral escape from multiple CTL responses can be used to predict the breadth and magnitude of HIV-specific CTL responses that need to be induced by vaccination to reduce (or even prevent) viral escape following HIV infection.