Mathematical Modeling of Streptococcus pneumoniae Colonization, Invasive Infection and Treatment

Abstract

Streptococcus pneumoniae (Sp) is a commensal bacterium that normally resides on the upper airway epithelium without causing infection. However, factors such as co-infection with influenza virus can impair the complex Sp-host interactions and the subsequent development of many life-threatening infectious and inflammatory diseases, including pneumonia, meningitis or even sepsis. With the increased threat of Sp infection due to the emergence of new antibiotic resistant Sp strains, there is an urgent need for better treatment strategies that effectively prevent progression of disease triggered by Sp infection, minimizing the use of antibiotics. The complexity of the host-pathogen interactions has left the full understanding of underlying mechanisms of Sp-triggered pathogenesis as a challenge, despite its critical importance in the identification of effective treatments. To achieve a systems-level and quantitative understanding of the complex and dynamically-changing host-Sp interactions, here we developed a mechanistic mathematical model describing dynamic interplays between Sp, immune cells, and epithelial tissues, where the host-pathogen interactions initiate. The model serves as a mathematical framework that coherently explains various in vitro and in vitro studies, to which the model parameters were fitted. Our model simulations reproduced the robust homeostatic Sp-host interaction, as well as three qualitatively different pathogenic behaviors: immunological scarring, invasive infection and their combination. Parameter sensitivity and bifurcation analyses of the model identified the processes that are responsible for qualitative transitions from healthy to such pathological behaviors. Our model also predicted that the onset of invasive infection occurs within less than 2 days from transient Sp challenges. This prediction provides arguments in favor of the use of vaccinations, since adaptive immune responses cannot be developed de novo in such a short time. We further designed optimal treatment strategies, with minimal strengths and minimal durations of antibiotics, for each of the three pathogenic behaviors distinguished by our model. The proposed mathematical framework will help to design better disease management strategies and new diagnostic markers that can be used to inform the most appropriate patient-specific treatment options.

Multiplicity of Mathematical Modeling Strategies to Search for Molecular and Cellular Insights into Bacteria Lung Infection

“…Moreover, these ODE model analysis methods can be integrated in workflows to investigate complex properties of biological systems (Nikolov et al, 2010 ). A very recent work created and analyzed a mathematical model of the Streptococcus pneumoniae lung infection (Domínguez-Hüttinger et al, 2017 ). It includes the interactions between the pathogen and the host like macrophages and neutrophils activation, bacteria clearance, epithelial cell barrier integrity and bacteria migration through the barrier to the vessels.…”

“…Pathogen rebounding is the proliferation of a pathogen after an initial decrease when it co-occurs with a second pathogen. In Smith et al (Domínguez-Hüttinger et al, 2017 ), upon infection with bacteria, the virus population rebounds due to the release of viruses that were latent in the immune and lung cells killed by the bacteria. In parallel, the model predicts an increase in the bacterial load due to the impairment of macrophage response provoked by the presence of the viruses.…”

BurnCare tablet trainer to enhance burn injury care and treatment

“…Hyper-inflammation drives the pathophysiology of thermal injury [ 10 – 12 ], the severity of which correlates well with TBSA burned [ 12 ]. BioGears maintains a dynamic model of acute inflammation based on the work of Chow [ 13 ], Reynolds [ 14 ], and Dominguez-Huttinger [ 15 ] that accounts for interactions between numerous pro- and anti-inflammatory mediators associated with burns—such as tumor necrosis factor (TNF) and interleukins-6 and 10 (IL-6, IL-10) [ 10 , 14 ]—and their collective contribution to endothelial damage. We have described the BioGears inflammation model previously in an in silico study of sepsis onset and treatment [ 6 ].…”

A Whole-Body Mathematical Model of Sepsis Progression and Treatment Designed in the BioGears Physiology Engine

“…As such, the diverse shock model does not consider an actively growing bacteria population. We therefore introduced the model of bacterial colonization and invasion derived by Domínguez-Hüttinger et al (2017) to the BioGears inflammation model. This invasion model assumes a Streptococcus pneumoniae inoculum colonizes in the lungs and diffuses across the apical epithelium into the bloodstream.…”

“…Other, higher-dimensional, models discretize the stages of inflammation into mass-action relationships involving specific pro- and anti-inflammatory mediators that have been implicated in sepsis. These models focus on varying aspects of systemic inflammation such as macrophage recruitment (Smith et al, 2011; Schirm et al, 2016), coagulation (Kumar, 2004), hypotension secondary to nitric oxide (NO) accumulation (Kumar, 2004; Chow et al, 2005; Brady, 2017), endothelial and epithelial tissue barrier characteristics (Reynolds, 2008; Domínguez-Hüttinger et al, 2017), and adaptive immunity (Shi et al, 2015). Most models published in this field consist of ever-growing systems of ordinary or partial differential equations, though some employ stochastic (Song et al, 2012) or machine learning (Mai et al, 2015) techniques.…”