Immunobiology and Infection Minisymposium

Rob de Boer

Universiteit Utrecht, Utrecht, The Netherlands

"Why do some populations of cells accumulate deuterium faster than lose it?"

Deuterium labelling experiments are often used to infer the kinetic properties (i.e., turnover rates, maintenance mechanisms) of various cell populations in vivo. For a homogeneous population that is at steady state, it is natural to expect that the population gains and loses labelled cells at the same rate. However, if the measured population is kinetically heterogeneous, it is natural to expect that the rate at which the labeling curve increases (i.e., the up-slope) is slower than the rate at which it decreases (i.e., the down-slope). Surprisingly, recent data from multiple deuterium-labelling experiments have the opposite property, and predict a gain of label that is faster than the rate of loss. Using various mathematical models, we search for mechanisms that can account for such unexpected labelling data. We show that the short-term labeling data can be explained when the deuterium remains available for a longer duration than it was administered. For the long-term labeling data we study models where (a) the population was considered to behave like a stem-cell population, or (b) the phases of the cell cycle were modeled with delay equations (i.e., with the Smith-Martin model). Both provide scenarios where the gain of label can be faster than its loss. However, the effect is small and these two models fail to give a good description of the experimental data. When we finally drop the assumption that the population remains at steady state, we readily explain the experimental data with a simple source, division and death model. This mechanism however requires that these populations are largely expanding by a source from a precursor compartment, and not by cell division (i.e., self-renewal).

Katia Koelle

Emory University, Atlanta, GA, USA

"Considering the consequences of cellular coinfection in within-host viral dynamics and modeling"

Within-host viral dynamic models often times categorize cells as infected or uninfected, similar to epidemiological microparasite models. However, in vitro and in vivo studies indicate that cellular coin- fection occurs frequently in influenza, HIV, and coronaviruses, among other viral pathogens. Here, I first discuss work from my group that develops simple models that allow for cellular coinfection in a scalable manner, with cellular multiplicity of infection affecting the phenotypes of infected cells, such as their death rate, viral production rate, and interferon induction rate. I then present work focusing on quantitatively characterizing the evolutionary consequences of cellular coinfection for newly arising mutations (both beneficial and deleterious). Our findings indicate that cellular coinfection decreases the ability of selection to act on individual mutations and results in genetic drift playing a larger role in modulating allele frequencies in a within-host viral population.

Carmen Molina Paris

University of Leeds, Leeds, UK

"A stochastic Model of Infection: Francisella T ularensis"

With a mouse infection model, agent-based computation and mathematical analysis, we study the pathogenesis of Francisella tularensis infection. A small initial number of bacteria enter host cells and proliferate inside them, eventually destroying the host cell and releasing numerous copies that infect other cells. Our analysis of disease progression is based on a stochastic model of a population of infectious agents inside one host cell, extending the birth-and-death process by the occurrence of catastrophes: cell rupture events that affect all bacteria in a cell simultaneously. Closed expressions are obtained for the survival function of an infected macrophage, the number of bacteria released as a function of time after infection, and total bacterial load. We compare our analysis with the results of agent-based computation and, via Approximate Bayesian Computation, with experimental measurements carried out after of murine aerosol infection with the virulent SCHU S4 strain of the bacterium. The posterior distribution is consistent with the estimate that the time between rounds of bacterial division is less than 6 hours in vivo.

Catherine Weathered

Purdue University, West Lafayette

"Mycobacterium Avium infection in the lungs: effects of bacterial phenotype and biofilm"

Mycobacterium avium complex (MAC), a type of nontuberculous mycobacteria, are environmental mi- crobes, capable of colonizing and infecting humans following inhalation of the bacteria. MAC-pulmonary disease is difficult to treat and prone to recurrence, and both incidence and prevalence are increasing. MAC form biofilms and diverse colonies in the environment. These biofilms can aid in epithelial cell invasion, cause premature apoptosis in macrophages, and inhibit antibiotic efficacy [4]. We hypothesize a balance of bacterial factors (phenotypic diversity and biofilm formation) and host immune factors (speed and magnitude of response) is key to establishing and prolonging infections in the lung. To test these hypotheses, we developed a 3D agent-based model (ABM) that incorporates known interactions between bacteria, biofilm and immune cells in virtual lung tissue. We implement our model in Repast Simphony. The simulation grid represents a length of lung airway with a layer of mucus. Bacterial agents are classified as either sessile or planktonic phenotypes that determine their behavior: biofilm formation, macrophage phagocytosis and replication rate. All bacterial agents and infected macrophages release a generic chemoattractant representing pathogen associated molecular patterns and chemokines respectively. These chemoattractants diffuse through the grid and are treated as continuous variables. Macrophages probabilistically follow the chemoattractant gradient, phagocytose bacteria, and accumulate apoptotic signals (representing hyperstimulation in the TNF-α pathway) through exposure to biofilm and internal bacteria. Model results show an early relationship between the initial number of macrophages or distance that chemoattractants diffuse, and the ratio of planktonic to sessile bacteria. Larger initial macrophage numbers result in a stronger and more sustained reduction in planktonic bacteria early after infection. However, as the infection progresses, the bacterial population is sustained by the sessile bacteria that are protected in biofilms or inside infected macrophages, allowing the planktonic population to recover. This effect is offset with further chemoattract diffusion, as the macrophages can clear the infection early or recruit more macrophages. Thus, the model predicts that both bacterial phenotypes and a suppressed immune responses affect the bacterial ability to survive, propagate, and eventually establish infection. Future directions of this work include exploring the continued role of phenotypes later in infection and treatment, and adding drug pharmacokinetics and cell-level pharmacodynamics to better understand the role of biofilm in treatment efficacy.