HTLV-I infection: A dynamic struggle between viral persistence and host immunity

Abstract

We develop a model for the host-virus dynamics of HTLV-I with target cell latency. A balance between proviral activation and latency aids viral persistence. Immune efficiency depends on rate of lysis and not on abundance of effector cells. Proviral activation may distinguish clinical status independent of proviral load. We hypothesise that crossing an activation threshold could increase risk of disease. a r t i c l e i n f o b s t r a c tHuman T-lymphotropic virus type I (HTLV-I) causes chronic infection for which there is no cure or neutralising vaccine. HTLV-I has been clinically linked to the development of adult T-cell leukaemia/ lymphoma (ATL), an aggressive blood cancer, and HAM/TSP, a progressive neurological and inflammatory disease. Infected individuals typically mount a large, persistently activated CD8 þ cytotoxic T-lymphocyte (CTL) response against HTLV-I-infected cells, but ultimately fail to effectively eliminate the virus. Moreover, the identification of determinants to disease manifestation has thus far been elusive. A key issue in current HTLV-I research is to better understand the dynamic interaction between persistent infection by HTLV-I and virus-specific host immunity. Recent experimental hypotheses for the persistence of HTLV-I in vivo have led to the development of mathematical models illuminating the balance between proviral latency and activation in the target cell population. We investigate the role of a constantly changing anti-viral immune environment acting in response to the effects of infected T-cell activation and subsequent viral expression. The resulting model is a four-dimensional, non-linear system of ordinary differential equations that describes the dynamic interactions among viral expression, infected target cell activation, and the HTLV-I-specific CTL response. The global dynamics of the model is established through the construction of appropriate Lyapunov functions. Examining the particular roles of viral expression and host immunity during the chronic phase of HTLV-I infection offers important insights regarding the evolution of viral persistence and proposes a hypothesis for pathogenesis.

Evaluation of the role of TAX, HBZ, and HTLV-1 proviral load on the survival of ATLL patients

“…This process is followed by transactivation of infected T-cells, during which TAX expression can be downregulated by other transcription factors, such as HBZ, to evade the host immune response [5]. Despite the higher TAX gene expression in ATLL patients, there was no direct correlation between TAX gene expression and PVL, which is indicative of the initiating role of this protein.…”

“…While HTLV-1 can transform and immortalize cells in vitro , only 2–5% of infected individuals go on to develop one of the two major pathogenic conditions, which occurs after a long-term period of dormancy [4]. The two major diseases associated with HTLV-1 infection are the antagonistic CD4+CD25+ T cell malignancy called adult T-cell leukemia/lymphoma (ATLL) and a host autoreactive inflammatory disorder called HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) [56]. …”

Dynamics analysis of an HTLV‐1 infection model with mitotic division of actively infected cells and delayed CTL immune response

“…Following from the works in Asquith and Bangham, Li and Lim proposed an HTLV‐1 dynamical model with latent infection. And then Lim and Maini further proposed a model of HTLV‐1 infection with latent infection and CTL immune response, where u ( t ) represents the density of latently infected CD4 + T cells at time t . The constants μ 1 > 0, μ 2 > 0, and μ 3 > 0 represent the death rates of healthy CD4 + T cells, latently infected CD4 + T cells, and actively infected CD4 + T cells, respectively, and the removal rate of CTLs is μ 4 > 0.…”

“…The constant σ > 0 is the rate at which latently infected CD4 + T cells translate to actively infected CD4 + T cells. Its biological mechanism is based on the following Figure . In Lim and Maini, it is assumed that , which corresponds to experimental evidence indicating that the producing rate of CD4 + T cells is generally lower than the rate of removal due to natural death.…”

Inferring clonal structure in HTLV-1-infected individuals: towards bridging the gap between analysis and visualization

“…To extract reliable fundamental meaning from a mathematical model and strengthen its predictive power, real experimental data must be introduced [31, 32]. Unlike other types of malignancies for which broad ranges of mathematical models are available [33], to our knowledge, there are few mathematical models for ATL [34–41], and only one visual model illustrating clonal expansion [27]. However, access to NGS data and the ability to retrieve clone size and integration site information have provided new opportunities to model clonal expansion in ATL.…”