An In Silico Modeling Approach to Understanding the Dynamics of Sarcoidosis
Citation: Aguda BD, Marsh CB, Thacker M, Crouser ED (
An In Silico Modeling Approach to Understanding the Dynamics of Sarcoidosis
Baltazar D. Aguda 0
Clay B. Marsh 0
Michael Thacker 0
Elliott D. Crouser 0
Mauricio Rojas, University of Pittsburgh, United States of America
0 1 Neuro-Oncology Branch, Center for Cancer Research, National Cancer Institute , Bethesda , Maryland, United States of America, 2 Division of Pulmonary , Allergy, Critical Care , and Sleep Medicine, The Ohio State University Medical Center , Columbus , Ohio, United States of America, 3 Department of Electrical and Computer Engineering, The Ohio State University Medical Center , Columbus, Ohio , United States of America
Background: Sarcoidosis is a polygenic disease with diverse phenotypic presentations characterized by an abnormal antigen-mediated Th1 type immune response. At present, progress towards understanding sarcoidosis disease mechanisms and the development of novel treatments is limited by constraints attendant to conducting human research in a rare disease in the absence of relevant animal models. We sought to develop a computational model to enhance our understanding of the pathological mechanisms of and predict potential treatments of sarcoidosis. Methodology/Results: Based upon the literature, we developed a computational model of known interactions between essential immune cells (antigen-presenting macrophages, effector and regulatory T cells) and cytokine mediators (IL-2, TNFa, IFNc) of granulomatous inflammation during sarcoidosis. The dynamics of these interactions are described by a set of ordinary differential equations. The model predicts bistable switching behavior which is consistent with normal (self-limited) and ''sarcoidosis-like'' (sustained) activation of the inflammatory components of the system following a single antigen challenge. By perturbing the influence of model components using inhibitors of the cytokine mediators, distinct clinically relevant disease phenotypes were represented. Finally, the model was shown to be useful for pre-clinical testing of therapies based upon molecular targets and dose-effect relationships. Conclusions/Significance: Our work illustrates a dynamic computer simulation of granulomatous inflammation scenarios that is useful for the investigation of disease mechanisms and for pre-clinical therapeutic testing. In lieu of relevant in vitro or animal surrogates, our model may provide for the screening of potential therapies for specific sarcoidosis disease phenotypes in advance of expensive clinical trials.
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Funding: Funding was provided by the American Thoracic Society/Foundation for Sarcoidosis Research (EDC). The funders had no role in study design, data
collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
Sarcoidosis is a chronic granulomatous disease of unknown cause,
for which relevant research models are lacking. Human research in
sarcoidosis is hindered by the existence of diverse clinical
phenotypes, presumably relating to genetic and environmental
variables [1]. Genetic variability may also explain the unpredictable
response to treatment among sarcoidosis patients [1]. Given the
genetic diversity of the disease, environmental variables (e.g.,
antigen exposures) and the lack of relevant animal models, it would
be necessary to recruit large numbers of patients, at a substantial
cost, to represent all of the sarcoidosis phenotypes using
conventional clinical research approaches. Alternatively, new
generation, high-throughput genetic screening platforms provide
an unprecedented opportunity to stratify the molecular basis of
sarcoidosis disease phenotypes with the ultimate goal of
individualizing therapy. To this end, it will be necessary to determine how
genetic variability influences disease pathogenesis and treatment.
In this report, we focus on sarcoidosis phenotypes that are
suspected to arise from defective antigen-dependent Th1 type
immune responses associated with deregulated interactions among
essential immune cells such as T effector cells, T regulatory cells,
and antigen-presenting macrophages. The interactions among
these cells are mediated by cytokines such as IL-2, IFNc, and
TNFa. We hypothesized that this complex interaction network
contained sufficient information for the investigation of normal
and sarcoidosis-like Th1 responses to antigens. Thus, we
developed a computational model to represent the dynamics of
this interaction network and its responses to perturbations. Our
results are the first demonstration of an in silico model of
granulomatous inflammation with potential applications for
mechanistic and therapeutic research relating to sarcoidosis and
other related diseases.
A minimal model for Th1 activation
The hallmark of sarcoidosis is the preponderance of Th1
immune response to poorly characterized antigens. The
differentiation of naive (...truncated)