Effect of synaptic cell-to-cell transmission and recombination on the evolution of double mutants in HIV

The role of cell-to-cell transmission in HIV infection: insights from a mathematical modeling approach

HIV infection remains a serious global public health problem. Although current drug treatment is effective and can reduce plasma viral loads below the level of detection, it cannot eradicate the virus. The reasons for the low virus persistence despite long-term therapy have not been fully elucidated. In addition, multiple HIV infection, i.e., infection of a cell by multiple viruses, is common and can facilitate viral recombination and mutations, evading the immune system and conferring resistance to drug treatment. The mechanisms for multiple HIV infection formation and their respective contributions remain unclear. To answer these questions, we developed a mathematical modeling framework that encompasses cell-free viral infection and cell-to-cell spread. We fit sub-models that only have one transmission route and the full model containing both to the multi-infection data from HIV-infected patients, and show that the multi-infection data can only be reproduced if these two transmission routes are both considered. Computer simulations with the best-fitting parameter values indicate that cell-to-cell spread leads to the majority of multiple infection and also accounts for the majority of overall infection. Sensitivity analysis shows that cell-to-cell spread has reduced susceptibility to treatment and may explain low HIV persistence. Taken together, this work indicates that cell-to-cell spread plays a crucial role in the development of HIV multi-infection and low HIV persistence despite long-term therapy, and therefore has important implications for understanding HIV pathogenesis and developing more effective treatment strategies to control or even eliminate the disease.

The HIV Env Glycoprotein Conformational States on Cells and Viruses

The HIV Env glycoprotein is the surface glycoprotein responsible for viral entry into CD4+ immune cells. During infection, Env also serves as a primary target for antibody responses, which are robust but unable to control virus replication. Immune evasion by HIV-1 Env appears to employ complex mechanisms to regulate what antigenic states are presented to the immune system. Immunodominant features appear to be distinct from epitopes that interfere with Env functions in mediating infection. Further, cell-cell transmission studies indicate that vulnerable conformational states are additionally hidden from recognition on infected cells, even though the presence of Env at the cell surface is required for viral infection through the virological synapse. Cell-cell infection studies support that Env on infected cells is presented in distinct conformations from that on virus particles. Here we review data regarding the regulation of conformational states of Env and assess how regulated sorting of Env within the infected cell may underlie mechanisms to distinguish Env on the surface of virus particles versus Env on the surface of infected cells. These mechanisms may allow infected cells to avoid opsonization, providing cell-to-cell infection by HIV with a selective advantage during evolution within an infected individual. Understanding how distinct Env conformations are presented on cells versus viruses may be essential to designing effective vaccine approaches and therapeutic strategies to clear infected cell reservoirs.

A hybrid stochastic-deterministic approach to explore multiple infection and evolution in HIV

To study viral evolutionary processes within patients, mathematical models have been instrumental. Yet, the need for stochastic simulations of minority mutant dynamics can pose computational challenges, especially in heterogeneous systems where very large and very small sub-populations coexist. Here, we describe a hybrid stochastic-deterministic algorithm to simulate mutant evolution in large viral populations, such as acute HIV-1 infection, and further include the multiple infection of cells. We demonstrate that the hybrid method can approximate the fully stochastic dynamics with sufficient accuracy at a fraction of the computational time, and quantify evolutionary end points that cannot be expressed by deterministic models, such as the mutant distribution or the probability of mutant existence at a given infected cell population size. We apply this method to study the role of multiple infection and intracellular interactions among different virus strains (such as complementation and interference) for mutant evolution. Multiple infection is predicted to increase the number of mutants at a given infected cell population size, due to a larger number of infection events. We further find that viral complementation can significantly enhance the spread of disadvantageous mutants, but only in select circumstances: it requires the occurrence of direct cell-to-cell transmission through virological synapses, as well as a substantial fitness disadvantage of the mutant, most likely corresponding to defective virus particles. This, however, likely has strong biological consequences because defective viruses can carry genetic diversity that can be incorporated into functional virus genomes via recombination. Through this mechanism, synaptic transmission in HIV might promote virus evolvability.

The evolution of human immunodeficiency virus within patients is an important part of the disease process. In particular, the presence of mutants that are resistant against anti-viral drugs can result in challenges to the long-term control of the infection. To study disease progression, computer simulations have been useful. However, in some cases these simulations can be difficult because of the complexity of the model. Here, we use a computational complexity reducing algorithm to simulate mutant dynamics in large populations, which can approximate the full model at a fraction of the time. The use of this algorithm allows us to study different transmission methods, viral processes that occur between virus strains within individual cells, and important quantities such as the mutant distribution or the probability of mutant existence at a given infected cell population size. We find that the direct synaptic cell-to-cell transmission of the virus through virological synapses can have strong biological consequences because it can promote potentially defective viruses that carry genetic diversity which can be incorporated into functional virus genomes during infection. Through this process, synaptic transmission in human immunodeficiency virus might promote virus evolvability.

Quantifying the dynamics of viral recombination during free virus and cell-to-cell transmission in HIV-1 infection

Recombination has been shown to contribute to human immunodeficiency virus-1 (HIV-1) evolution in vivo, but the underlying dynamics are extremely complex, depending on the nature of the fitness landscapes and of epistatic interactions. A less well-studied determinant of recombinant evolution is the mode of virus transmission in the cell population. HIV-1 can spread by free virus transmission, resulting largely in singly infected cells, and also by direct cell-to-cell transmission, resulting in the simultaneous infection of cells with multiple viruses. We investigate the contribution of these two transmission pathways to recombinant evolution, by applying mathematical models to in vitro experimental data on the growth of fluorescent reporter viruses under static conditions (where both transmission pathways operate), and under gentle shaking conditions, where cell-to-cell transmission is largely inhibited. The parameterized mathematical models are then used to extrapolate the viral evolutionary dynamics beyond the experimental settings. Assuming a fixed basic reproductive ratio of the virus (independent of transmission pathway), we find that recombinant evolution is fastest if virus spread is driven only by cell-to-cell transmission and slows down if both transmission pathways operate. Recombinant evolution is slowest if all virus spread occurs through free virus transmission. This is due to cell-to-cell transmission 1, increasing infection multiplicity; 2, promoting the co-transmission of different virus strains from cell to cell; and 3, increasing the rate at which point mutations are generated as a result of more reverse transcription events. This study further resulted in the estimation of various parameters that characterize these evolutionary processes. For example, we estimate that during cell-to-cell transmission, an average of three viruses successfully integrated into the target cell, which can significantly raise the infection multiplicity compared to free virus transmission. In general, our study points towards the importance of infection multiplicity and cell-to-cell transmission for HIV evolution.