On the Role of CD8+ T Cells in Determining Recovery Time from Influenza Virus Infection

Abstract

Myriad experiments have identified an important role for CD8+ T cell response mechanisms in determining recovery from influenza A virus infection. Animal models of influenza infection further implicate multiple elements of the immune response in defining the dynamical characteristics of viral infection. To date, influenza virus models, while capturing particular aspects of the natural infection history, have been unable to reproduce the full gamut of observed viral kinetic behavior in a single coherent framework. Here, we introduce a mathematical model of influenza viral dynamics incorporating innate, humoral, and cellular immune components and explore its properties with a particular emphasis on the role of cellular immunity. Calibrated against a range of murine data, our model is capable of recapitulating observed viral kinetics from a multitude of experiments. Importantly, the model predicts a robust exponential relationship between the level of effector CD8+ T cells and recovery time, whereby recovery time rapidly decreases to a fixed minimum recovery time with an increasing level of effector CD8+ T cells. We find support for this relationship in recent clinical data from influenza A (H7N9) hospitalized patients. The exponential relationship implies that people with a lower level of naive CD8+ T cells may receive significantly more benefit from induction of additional effector CD8+ T cells arising from immunological memory, itself established through either previous viral infection or T cell-based vaccines.

Host‐pathogen kinetics during influenza infection and coinfection: insights from predictive modeling

“…Nevertheless, several studies have challenged whether the approximation is accurate and how it relates to different host responses, such as type I interferons (IFN‐α and IFN‐β) (A.M. Smith, unpublished data). Some studies have attempted to establish a comprehensive view of the host response while others have taken a more focused approach . One benefit of models with reduced complexity is the availability of analytical tools that can facilitate a robust interpretation of the dynamics.…”

“…During model fitting, the log 10 infectious viral load, which is typically in units of 50% tissue culture infectious dose (TCID 50 ) or plaque forming units (PFU), is compared to the log 10 output of the model. A variety of data fitting algorithms have been used, including adaptive simulated annealing (ASA), Monte Carlo Markov Chain (MCMC), Gaussian processes (GP), and maximum likelihood estimation (MLE) . Until recently, it was relatively well accepted that the choice of estimation scheme is not critical.…”

“…The inability of some models to predict the response in perturbed conditions may be due to the simplicity of the model or a misinterpretation of the data. For dose‐dependent kinetics, including features of the host response can improve accuracy and predictive power …”

“…Some studies have attempted to establish a comprehensive view of the host response 15,35,49,50 while others have taken a more focused approach. 14,17,26,28,34 One benefit of models with reduced complexity is the availability of analytical tools that can facilitate a robust interpretation of the dynamics.…”

“…have made it possible to dissect critical mechanisms that drive the infection. The models have successfully quantified and predicted the viral load kinetics from clinical and experimental infections, [12][13][14][15][16][17][18][19][20][21][22][23][24][25] the symptoms that arise during infection, 12,13 the dynamics and efficiency of different host immune responses, 14,17,18,[25][26][27][28][29][30][31][32][33][34] the effect of different viral and host factors, 15,16,[20][21][22]35 the efficacy and design of vaccines and antiviral therapies, 14,[19][20][21][22][36][37][38][39][40] and the mechanisms of coinfection between influenza viruses and other viruses or bacteria. [41][42]…”