Immunological transitions in response to antigenic mutation during viral infection

Virus evolution within patients increases pathogenicity

Viruses like the human immunodeficiency virus (HIV), the hepatitis B virus (HBV), the hepatitis C virus (HCV) and many others undergo numerous rounds of inaccurate reproduction within an infected host. The resulting viral quasispecies is heterogeneous and sensitive to any selection pressure. Here we extend earlier work by showing that for a wide class of models describing the interaction between the virus population and the immune system, virus evolution has a well-defined direction toward increased pathogenicity. In particular, we study virus-induced impairment of the immune response and certain cross-reactive stimulation of specific immune responses. For eight different mathematical models, we show that virus evolution reduces the equilibrium abundance of uninfected cells and increases the rate at which uninfected cells are infected. Thus, in general, virus evolution makes things worse. An idea for combating HIV infection, however, is constructing a virus mutant that could outcompete the existing infection without being pathogenic itself.

Some basic properties of immune selection

We analyze models for the evolutionary dynamics of viral or other infectious agents within a host. We study how the invasion of a new strain affects the composition and diversity of the viral population. We show that--under strain-specific immunity--the equilibrium abundance of uninfected cells declines during viral evolution. In addition, for cytotoxic immunity the absolute force of infection, and for non-cytotoxic immunity the absolute cellular virulence increases during viral evolution. We prove global stability by means of Lyapunov functions. These unidirectional trends of virus evolution under immune selection do not hold for general cross-reactive immune responses, which introduce frequency-dependent selection among viral strains. Therefore, appropriate cross-reactive immunity can lead to a viral evolution within a host which limits the extent of the disease.

Structural identifiability analysis of some highly structured families of statespace models using differential algebra

In this paper we identify biologically relevant families of models whose structural identifiability analysis could not be performed with available techniques directly. The models considered come from both the immunological and epidemiological literature.

Computer simulations of slow progression of human immunodeficiency virus infection and relapse during anti-HIV treatment with reverse transcriptase inhibitors and protease inhibitors

Human immunodeficiency virus (HIV) infection and acquired immunodeficiency syndrome (AIDS) have been very serious problems since the 1980s. The progression of HIV infection into AIDS can be suppressed to some extent with reverse transcriptase inhibitors (RTIs) and protease inhibitors (PIs); however, there are some serious problems with treatments using the anti-HIV drugs (e.g. very high expense, complicated administration, and drug resistance). Hence, more studies on HIV and the development of more effective anti-HIV treatments are required. We consider it important to understand the complex dynamics involved in HIV infection, and we therefore propose new mathematical models of HIV infection. In the modeling, we have paid attention to the nonlinear relations between stimuli and responses (i.e., when responses are plotted against the logarithm of stimuli, a sigmoid curve is obtained), and to lymphoid organs which seem more important than the blood compartment (i.e., lymphoid organs are major reservoirs of HIV virions and contain most of the lymphocytes). Using the models, we have found that viral antigenic mutation plays an important role in the slow progression in the chronic phase of HIV infection. We have also found that viral antigenic mutation can cause relapse of HIV infection when the inhibition rate of anti-HIV drugs is low and that viral antigenic mutation cannot cause relapse when the inhibition rate is high.