The pressing need for within-host models of the pulmonary immune response

Thursday, June 17 at 09:30am (PDT)
Thursday, June 17 at 05:30pm (BST)
Friday, June 18 01:30am (KST)

SMB2021 SMB2021 Follow Thursday (Friday) during the "MS19" time block.
Note: this minisymposia has multiple sessions. The second session is MS20-IMMU (click here).

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Luis Sordo Vieira (Division of Pulmonary, Critical Care, and Sleep Medicine, University of Florida, United States), Marissa Renardy (University of Michigan/Applied BioMath, United States), Tracy Stepien (Department of Mathematics, University of Florida, United States)


Respiratory pathogens and other pulmonary conditions will remain as leading threats to human health in the foreseeable future. The COVID-19 pandemic exemplified how mathematical models are integral to a quick and effective response to mitigating respiratory infections. Although many epidemiological models exist for the spread of disease, there is a need for within-host computational models of the immune response to respiratory pathogens that can be personalized to predict host-centric responses. This minisymposium will feature talks by clinicians discussing the leading problems in pulmonology, experimentalists discussing available tools for data acquisition including animal models, and computational modelers discussing models of the immune response to leading respiratory pathogens and related conditions.

Borna Mehrad

(Division of Pulmonary, Critical Care, and Sleep Medicine, University of Florida, United States)
"Big Problems in Pulmonary Medicine: A Research Agenda"
According to the World Health Organization, 3 of the 10 leading causes of death worldwide are lung diseases. In order, these are pneumonia (in which category I include COVID-19 and tuberculosis), chronic obstructive pulmonary disease, and lung cancer — these illnesses are a good place to start a discussion about a research agenda about the big problems in pulmonary medicine. In this talk, I will give an overview of each illness from a clinical and biological perspective, discuss some recent discoveries in each field, and end with key unresolved questions for each category.

Josh Mattila

(University of Pittsburgh, United States)
"Converting pathology into data points and back again: using systems immunology to investigate cause-effect relationships in tuberculosis"
Tuberculosis is caused by Mycobacterium tuberculosis (Mtb), a bacterium that infects nearly a third of the world’s population. The human immune system is very effective at combatting Mtb and most infected people never experience symptomatic TB but there are still more than 10 million new TB cases and almost 2 million deaths from TB per year. Granulomas are the hallmark of TB and these multicellular lesions form in Mtb-infected tissues. Under optimal conditions, granulomas prevent bacterial dissemination and can generate sterilizing immunity but under suboptimal conditions, granulomas are sites of bacterial persistence and replication. Unfortunately, it is difficult to identify correlates of immunity in TB because granulomas occur in tissues that cannot be sampled and most of our information on immunity in TB comes from peripheral blood or murine TB models, neither of which replicate fully immunity in granulomas. Granulomas from experimentally-infected nonhuman primates (NHP) offer a human-like alternative but inter-granuloma heterogeneity and difficulties assessing the temporal trajectory of granuloma maturation and function make it difficult to interpret data from NHP granulomas. Computational models of granulomas, powered by biologic data obtained from ex vivo wet-lab studies on NHP granulomas, can model aspects of granuloma biology that correlate with protective or detrimental immunity. Here, I review how we have used biologic data from NHP granulomas to calibrate and validate GranSim, a computational granuloma model developed by the Kirschner Lab at the University of Michigan.

Maral Budak

(University of Michigan Medical School, United States)
"Optimization of multidrug therapies for tuberculosis using a multi-scale computational model"
Tuberculosis (TB) is caused by the inhalation of Mycobacterium tuberculosis (Mtb), leading to ~1.5 million deaths every year. Mtb mainly infects lungs and triggers the formation of dense cellular structures composed of immune cells, bacteria, and dead tissue, called granulomas. The complex structure of granulomas prevents the effective penetration of antibiotics used to treat TB. Moreover, the heterogeneity of granulomas gives rise to various microenvironments for Mtb, where bacteria acquire different metabolic states that determine the potency of antibiotics either singly or in combination. Due to these reasons, TB treatment requires treatment with multiple antibiotics over long periods (6-9 months), causing prolonged side effects and compliance issues. Optimizing multidrug therapies and regimens for TB is essential to treat TB more effectively. In this study, we combined in vitro drug interaction predictions within GranSim, our computational model of granuloma formation and drug activity that simulates spatio-temporal granuloma drug dynamics. By systematically testing drug candidate regimens and considering drug interactions, we predict optimal drug regimens to be tested in vivo. This study will potentially lead to the discovery of more effective drug regimens that shorten the treatment window and have fewer side effects.

Henrique de Assis Lopes Ribeiro

(Division of Pulmonary, Critical Care, and Sleep Medicine, University of Florida, United States)
"Computational Modeling Reveals the Role of Macrophages in Respiratory A. fumigatus Infection in Immunocompromised Hosts"
Fungal infections of the respiratory system are a life-threatening complication for immunocompromised patients. Invasive pulmonary aspergillosis, caused by the airborne mold Aspergillus fumigatus, has a mortality rate of up to 50% in this patient population. The lack of neutrophils, a common immunodeficiency caused by, e.g.,chemotherapy, disables a mechanism of sequestering iron from the pathogen, an important virulence factor. This paper shows that a key reason why macrophages are unable to control the infection in the absence of neutrophils is the onset of hemorrhaging, as the fungus punctures the alveolar wall. The result is that the fungus gains access to heme-bound iron. At the same time, the macrophage response to the fungus is impaired. We show that these two phenomena together enable the infection to be successful. A key technology used in this work is a novel dynamic computational model used as a virtual laboratory to guide the discovery process. The paper shows how it can be used further to explore potential therapeutics to strengthen the macrophage response.

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Virtual conference of the Society for Mathematical Biology, 2021.