Immune responses to pathogens are complex and not well understood in many diseases, and this is especially true for infections by persistent pathogens. One mechanism that allows for long-term control of infection while also preventing an over-zealous inflammatory response from causing extensive tissue damage is for the immune system to balance pro- and anti-inflammatory cells and signals. This balance is dynamic and the immune system responds to cues from both host and pathogen, maintaining a steady state across multiple scales through continuous feedback. Identifying the signals, cells, cytokines, and other immune response factors that mediate this balance over time has been difficult using traditional research strategies. Computational modeling studies based on data from traditional systems can identify how this balance contributes to immunity. Here we provide evidence from both experimental and mathematical/computational studies to support the concept of a dynamic balance operating during persistent and other infection scenarios. We focus mainly on tuberculosis, currently the leading cause of death due to infectious disease in the world, and also provide evidence for other infections. A better understanding of the dynamically balanced immune response can help shape treatment strategies that utilize both drugs and host-directed therapies.

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Biopolymeric matrices can impede transport of nanoparticulates and pathogens by entropic or direct adhesive interactions, or by harnessing “third-party” molecular anchors to crosslink nanoparticulates to matrix constituents. The trapping potency of anchors is dictated by association rates and affinities to both nanoparticulates and matrix; the popular dogma is that long-lived, high-affinity bonds to both species facilitate optimal trapping. Here we present a contrasting paradigm combining experimental evidence (using IgG antibodies and Matrigel®), a theoretical framework (based on multiple timescale analysis), and computational modeling. Anchors that bind and unbind rapidly from matrix accumulate on nanoparticulates much more quickly than anchors that form high-affinity, long-lived bonds with matrix, leading to markedly greater trapping potency of multiple invading species without saturating matrix trapping capacity. Our results provide a blueprint for engineering molecular anchors with finely tuned affinities to effectively enhance the barrier properties of biogels against diverse nanoparticulate species.

Immunoglobulin G (IgG) antibodies that trap viruses in cervicovaginal mucus (CVM) via adhesive interactions between IgG-Fc and mucins have recently emerged as a promising strategy to block vaginally transmitted infections. The array of IgG bound to a virus particle appears to trap the virus by making multiple weak affinity bonds to the fibrous mucins that form the mucus gel. However, the antibody characteristics that maximize virus trapping and minimize viral infectivity remain poorly understood. Toward this goal, we developed a mathematical model that takes into account physiologically relevant spatial dimensions and time scales, binding, and unbinding rates between IgG and virions and between IgG and mucins, as well as the respective diffusivities of virions and IgG in semen and CVM. We then systematically explored the IgG–antigen and IgG–mucin binding and unbinding rates that minimize the flux of infectious HIV arriving at the vaginal epithelium. Surprisingly, contrary to common intuition that infectivity would drop monotonically with increasing affinities between IgG and HIV, and between IgG and mucins, our model suggests maximal trapping of HIV and minimal flux of HIV to the epithelium are achieved with IgG molecules that exhibit (i) rapid antigen binding (high kon) rather than very slow unbinding (low koff), that is, high-affinity binding to the virion, and (ii) relatively weak affinity with mucins. These results provide important insights into the design of more potent “mucotrapping” IgG for enhanced protection against vaginally transmitted infections. The model is adaptable to other pathogens, mucosal barriers, geometries, and kinetic and diffusional effects, providing a tool for hypothesis testing and producing quantitative insights into the dynamics of immune-mediated protection.