CellularPotts.jl: simulating multiscale cellular models in Julia.

SUMMARY: CellularPotts.jl is a software package written in Julia to simulate biological cellular processes such as division, adhesion, and signaling. Accurately modeling and predicting these simple processes is crucial because they facilitate more complex biological phenomena related to important disease states like tumor growth, wound healing, and infection. Here we take advantage of Cellular Potts Modeling to simulate cellular interactions and combine them with differential equations to model dynamic cell signaling patterns. These models are advantageous over other approaches because they retain spatial information about each cell while remaining computationally efficient at larger scales. Users of this package define three key inputs to create valid model definitions: a 2- or 3-dimensional space, a table describing the cells to be positioned in that space, and a list of model penalties that dictate cell behaviors. Models can then be evolved over time to collect statistics, simulated repeatedly to investigate how changing a specific property impacts cellular behavior, and visualized using any of the available plotting libraries in Julia. AVAILABILITY AND IMPLEMENTATION: The CellularPotts.jl package is released under the MIT license and is available at https://github.com/RobertGregg/CellularPotts.jl. An archived version of the code (v0.3.2) at time of submission can also be found at https://doi.org/10.5281/zenodo.10407783.

Early Evidence of Chronic Obstructive Pulmonary Disease Obscured by Race-Specific Prediction Equations.

Rationale: The identification of early chronic obstructive pulmonary disease (COPD) is essential to appropriately counsel patients regarding smoking cessation, provide symptomatic treatment, and eventually develop disease-modifying treatments. Disease severity in COPD is defined using race-specific spirometry equations. These may disadvantage non-White individuals in diagnosis and care. Objectives: Determine the impact of race-specific equations on African American (AA) versus non-Hispanic White individuals. Methods: Cross-sectional analyses of the COPDGene (Genetic Epidemiology of Chronic Obstructive Pulmonary Disease) cohort were conducted, comparing non-Hispanic White (n = 6,766) and AA (n = 3,366) participants for COPD manifestations. Measurements and Main Results: Spirometric classifications using race-specific, multiethnic, and "race-reversed" prediction equations (NHANES [National Health and Nutrition Examination Survey] and Global Lung Function Initiative "Other" and "Global") were compared, as were respiratory symptoms, 6-minute-walk distance, computed tomography imaging, respiratory exacerbations, and St. George's Respiratory Questionnaire. Application of different prediction equations to the cohort resulted in different classifications by stage, with NHANES and Global Lung Function Initiative race-specific equations being minimally different, but race-reversed equations moving AA participants to more severe stages and especially between the Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage 0 and preserved ratio impaired spirometry groups. Classification using the established NHANES race-specific equations demonstrated that for each of GOLD stages 1-4, AA participants were younger, had fewer pack-years and more current smoking, but had more exacerbations, shorter 6-minute-walk distance, greater dyspnea, and worse BODE (body mass index, airway obstruction, dyspnea, and exercise capacity) scores and St. George's Respiratory Questionnaire scores. Differences were greatest in GOLD stages 1 and 2. Race-reversed equations reclassified 774 AA participants (43%) from GOLD stage 0 to preserved ratio impaired spirometry. Conclusions: Race-specific equations underestimated disease severity among AA participants. These effects were particularly evident in early disease and may result in late detection of COPD.

Identification of factors directly linked to incident chronic obstructive pulmonary disease: A causal graph modeling study.

BACKGROUND: Beyond exposure to cigarette smoking and aging, the factors that influence lung function decline to incident chronic obstructive pulmonary disease (COPD) remain unclear. Advancements have been made in categorizing COPD into emphysema and airway predominant disease subtypes; however, predicting which healthy individuals will progress to COPD is difficult because they can exhibit profoundly different disease trajectories despite similar initial risk factors. This study aimed to identify clinical, genetic, and radiological features that are directly linked-and subsequently predict-abnormal lung function. METHODS AND FINDINGS: We employed graph modeling on 2,643 COPDGene participants (aged 45 to 80 years, 51.25% female, 35.1% African Americans; enrollment 11/2007-4/2011) with smoking history but normal spirometry at study enrollment to identify variables that are directly linked to future lung function abnormalities. We developed logistic regression and random forest predictive models for distinguishing individuals who maintain lung function from those who decline. Of the 131 variables analyzed, 6 were identified as informative to future lung function abnormalities, namely forced expiratory flow in the middle range (FEF25-75%), average lung wall thickness in a 10 mm radius (Pi10), severe emphysema, age, sex, and height. We investigated whether these features predict individuals leaving GOLD 0 status (normal spirometry according to Global Initiative for Obstructive Lung Disease (GOLD) criteria). Linear models, trained with these features, were quite predictive (area under receiver operator characteristic curve or AUROC = 0.75). Random forest predictors performed similarly to logistic regression (AUROC = 0.7), indicating that no significant nonlinear effects were present. The results were externally validated on 150 participants from Specialized Center for Clinically Oriented Research (SCCOR) cohort (aged 45 to 80 years, 52.7% female, 4.7% African Americans; enrollment: 7/2007-12/2012) (AUROC = 0.89). The main limitation of longitudinal studies with 5- and 10-year follow-up is the introduction of mortality bias that disproportionately affects the more severe cases. However, our study focused on spirometrically normal individuals, who have a lower mortality rate. Another limitation is the use of strict criteria to define spirometrically normal individuals, which was unavoidable when studying factors associated with changes in normalized forced expiratory volume in 1 s (FEV1%predicted) or the ratio of FEV1/FVC (forced vital capacity). CONCLUSIONS: This study took an agnostic approach to identify which baseline measurements differentiate and predict the early stages of lung function decline in individuals with previous smoking history. Our analysis suggests that emphysema affects obstruction onset, while airway predominant pathology may play a more important role in future FEV1 (%predicted) decline without obstruction, and FEF25-75% may affect both.

Agent-based modeling reveals benefits of heterogeneous and stochastic cell populations during cGAS-mediated IFNß production.

MOTIVATION: The cGAS pathway is a component of the innate immune system responsible for the detection of pathogenic DNA and upregulation of interferon beta (IFNß). Experimental evidence shows that IFNß signaling occurs in highly heterogeneous cells and is stochastic in nature; however, the benefits of these attributes remain unclear. To investigate how stochasticity and heterogeneity affect IFNß production, an agent-based model is developed to simulate both DNA transfection and viral infection. RESULTS: We show that heterogeneity can enhance IFNß responses during infection. Furthermore, by varying the degree of IFNß stochasticity, we find that only a percentage of cells (20-30%) need to respond during infection. Going beyond this range provides no additional protection against cell death or reduction of viral load. Overall, these simulations suggest that heterogeneity and stochasticity are important for moderating immune potency while minimizing cell death during infection. AVAILABILITY AND IMPLEMENTATION: Model repository is available at: https://github.com/ImmuSystems-Lab/AgentBasedModel-cGASPathway. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Pathogenesis of Influenza A(H7N9) Virus in Aged Nonhuman Primates.

The avian influenza A(H7N9) virus has caused high mortality rates in humans, especially in the elderly; however, little is known about the mechanistic basis for this. In the current study, we used nonhuman primates to evaluate the effect of aging on the pathogenicity of A(H7N9) virus. We observed that A(H7N9) virus infection of aged animals (defined as age 20-26 years) caused more severe symptoms than infection of young animals (defined as age 2-3 years). In aged animals, lung inflammation was weak and virus infection was sustained. Although cytokine and chemokine expression in the lungs of most aged animals was lower than that in the lungs of young animals, 1 aged animal showed severe symptoms and dysregulated proinflammatory cytokine and chemokine production. These results suggest that attenuated or dysregulated immune responses in aged animals are responsible for the severe symptoms observed among elderly patients infected with A(H7N9) virus.

Mathematical modeling of the cGAS pathway reveals robustness of DNA sensing to TREX1 feedback.

Cyclic GMP-AMP synthase (cGAS) has recently been identified as the primary protein that detects cytosolic double stranded DNA to invoke a type I interferon response. The cGAS pathway is vital in the recognition of DNA encoded viruses as well as self-DNA leaked from the nucleus of damaged cells. Currently, the dynamics regulating the cGAS pathway are poorly understood; limiting our knowledge of how DNA-induced immune responses are regulated. Using systems biology approaches, we formulated a mathematical model to describe the dynamics of this pathway and examine the resulting system-level emergent properties. Unknown model parameters were fit to data compiled from literature using a Parallel Tempering Markov Chain Monte Carlo (PT-MCMC) approach, resulting in an ensemble of parameterized models. A local sensitivity analysis demonstrated that parameter sensitivity trends across model ensembles were independent of the select parameterization. An in-silico knock-down of TREX1 found that the interferon response is highly robust, showing that complete inhibition is necessary to induce chemical conditions consistent with chronic inflammation. Lastly, we demonstrate that the model recapitulates interferon expression data resulting from small molecule inhibition of cGAS. Overall, the importance of this model is exhibited in its capacity to identify sensitive components of the cGAS pathway, generate testable hypotheses, and confirm experimental observations.

Spatial and temporal evolution of lung granulomas in a cynomolgus macaque model of Mycobacterium tuberculosis infection.

BACKGROUND: Little is known about granuloma progression of Mycobacterium tuberculosis infection in humans. Using serial positron emission tomography and computed tomography (PET/CT) of an animal model that recapitulates human infection with M. tuberculosis, we are able to track lung granulomas. OBJECTIVE: We characterized the spatial and temporal pattern of granuloma formation during primary infection and reactivation. METHODS: Serial PET/CT was performed on cynomolgus macaques (n = 28) during primary and reactivation M. tuberculosis infection. Distances between granulomas during the first six weeks post infection ("primary" granulomas) were compared to new granulomas that developed afterwards ("secondary" granulomas) using nearest neighbor analysis during primary infection, reactivation and between different routes of infection. RESULTS: Secondary granulomas developed within 2 cm of a primary granuloma within the same lung lobe with 80% probability during the course of primary infection, and this same pattern was observed during reactivation of latent infection after immune suppression. Using a logistic growth function, we were able to predict the maximum number of granulomas that would develop over the course of infection with good correlation (R2 = 0.96). CONCLUSION: These data provide important insights into the dynamic patterns of bacterial dissemination during the earliest phases of primary infection and reactivation tuberculosis.