Interdisciplinary characterization of T cell dynamics in HIV infection

Abstract

HIV has caused one of the most devastating pandemics in modern medicine. HIV infects and kills on of the central players in effector immunity, CD4+ T cells, that provide helper mechanisms to all arms of the immune system. Although the virus indirectly affects most cells of the immune system, CD4+ and CD8+ T cells in particular become highly dysfunctional and show traits of severe immune pathology during the infection. These cells are of importance in adaptive immunity and recognize through their T cell receptors foreign antigens that are presented on MHC molecules. In the absence of normal T cell dynamics and homeostasis, host effector immunity collapses and most individuals develop AIDS, without antiretroviral therapy.

The growing number of immunological variables measured today poses challenges to studying T cell dynamics in HIV infection. However, with the introduction of new techniques within bioinformatics, we now possess statistical tools to analyse combined measurements of T cell pathology, epitope targeting and dysfunction in the context of HIV infection. In all of these studies, multi-parametric flow cytometry and advanced bioinformatics were thus combined to study traits of T cell dynamics in HIV infection. By examining a broad range of T cell markers, we concluded in paper I that the CD4/CD8 ratio correlated with a significantly increased number of pathological T cell populations and was associated with CD4 recovery 2 years after ART initiation. These data indicate that the CD4/CD8 ratio would be a suitable clinical predictor of combined T cell pathology in HIV infection.

By developing a novel epitope selection algorithm in paper II, we aimed to identify optimal MHC class II-restricted HIV epitopes with broad viral and host coverage. Employing both immunological and virological approaches, a set of peptides was shown to induce broad HIV-specific CD4+ T cell responses, where the number of targeted Gag epitopes was inversely correlated with HIV viral load. In order to further trace events of HIV disease progression, we investigated whether the combined pattern of HIV evolution and CD8+ T cell functionality could explain the risk of HIV disease progression in HLA-B*5701+ patients (paper III). HIV Gag sequence diversity was shown to be lower and multi-functional responses higher against wild-type and autologous HLA-B*5701-restricted epitopes in subjects of low risk of disease progression. Both of these studies highlight the power of multidisciplinary approaches, integrating complex evolutionary and immunological data, to understand the mechanisms underlying T cell dysfunction and pathogenesis.

To further clarify why HIV-specific CD8+ T cells exhibit severe dysfunctionalcharacteristics in both treated and untreated HIV infection, we studied in paper IV the role of two central T-box transcription factors (T-bet and Eomes) using combined flow cytometry and bioinformatics. It was shown that HIV-specific CD8+ T cells almost exclusively have highly elevated levels of Eomes, but lower T-bet expression, which is associated with up-regulation of numerous inhibitory receptors, impaired functional characteristics and a transitional memory differentiation status. Surprisingly, these features were retrained despite any years on ART, implicating that the relationship between T-bet and Eomes might partly explain the inability of CD8+ T cells to control viral rebound post ART cessation.

In summary, this thesis has combined the knowledge of immunology andvirology with the help of bioinformatics to study T cell dynamics in HIV infection. This interdisciplinary approach has increased our knowledge of factors that are linkedto T cell pathology, risk of disease progression and impaired T cell functionality.