1
|
Haase M, Comlekoglu T, Petrucciani A, Peirce SM, Blemker SS. Agent-based model demonstrates the impact of nonlinear, complex interactions between cytokinces on muscle regeneration. eLife 2024; 13:RP91924. [PMID: 38828844 PMCID: PMC11147512 DOI: 10.7554/elife.91924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024] Open
Abstract
Muscle regeneration is a complex process due to dynamic and multiscale biochemical and cellular interactions, making it difficult to identify microenvironmental conditions that are beneficial to muscle recovery from injury using experimental approaches alone. To understand the degree to which individual cellular behaviors impact endogenous mechanisms of muscle recovery, we developed an agent-based model (ABM) using the Cellular-Potts framework to simulate the dynamic microenvironment of a cross-section of murine skeletal muscle tissue. We referenced more than 100 published studies to define over 100 parameters and rules that dictate the behavior of muscle fibers, satellite stem cells (SSCs), fibroblasts, neutrophils, macrophages, microvessels, and lymphatic vessels, as well as their interactions with each other and the microenvironment. We utilized parameter density estimation to calibrate the model to temporal biological datasets describing cross-sectional area (CSA) recovery, SSC, and fibroblast cell counts at multiple timepoints following injury. The calibrated model was validated by comparison of other model outputs (macrophage, neutrophil, and capillaries counts) to experimental observations. Predictions for eight model perturbations that varied cell or cytokine input conditions were compared to published experimental studies to validate model predictive capabilities. We used Latin hypercube sampling and partial rank correlation coefficient to identify in silico perturbations of cytokine diffusion coefficients and decay rates to enhance CSA recovery. This analysis suggests that combined alterations of specific cytokine decay and diffusion parameters result in greater fibroblast and SSC proliferation compared to individual perturbations with a 13% increase in CSA recovery compared to unaltered regeneration at 28 days. These results enable guided development of therapeutic strategies that similarly alter muscle physiology (i.e. converting extracellular matrix [ECM]-bound cytokines into freely diffusible forms as studied in cancer therapeutics or delivery of exogenous cytokines) during regeneration to enhance muscle recovery after injury.
Collapse
Affiliation(s)
- Megan Haase
- University of VirginiaCharlottesvilleUnited States
| | | | | | | | | |
Collapse
|
2
|
Nanda P, Budak M, Michael CT, Krupinsky K, Kirschner DE. Development and Analysis of Multiscale Models for Tuberculosis: From Molecules to Populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.13.566861. [PMID: 38014103 PMCID: PMC10680629 DOI: 10.1101/2023.11.13.566861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Although infectious disease dynamics are often analyzed at the macro-scale, increasing numbers of drug-resistant infections highlight the importance of within-host modeling that simultaneously solves across multiple scales to effectively respond to epidemics. We review multiscale modeling approaches for complex, interconnected biological systems and discuss critical steps involved in building, analyzing, and applying such models within the discipline of model credibility. We also present our two tools: CaliPro, for calibrating multiscale models (MSMs) to datasets, and tunable resolution, for fine- and coarse-graining sub-models while retaining insights. We include as an example our work simulating infection with Mycobacterium tuberculosis to demonstrate modeling choices and how predictions are made to generate new insights and test interventions. We discuss some of the current challenges of incorporating novel datasets, rigorously training computational biologists, and increasing the reach of MSMs. We also offer several promising future research directions of incorporating within-host dynamics into applications ranging from combinatorial treatment to epidemic response.
Collapse
|
3
|
Larkins-Ford J, Aldridge BB. Advances in the design of combination therapies for the treatment of tuberculosis. Expert Opin Drug Discov 2023; 18:83-97. [PMID: 36538813 PMCID: PMC9892364 DOI: 10.1080/17460441.2023.2157811] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 12/08/2022] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Tuberculosis requires lengthy multi-drug therapy. Mycobacterium tuberculosis occupies different tissue compartments during infection, making drug access and susceptibility patterns variable. Antibiotic combinations are needed to ensure each compartment of infection is reached with effective drug treatment. Despite drug combinations' role in treating tuberculosis, the design of such combinations has been tackled relatively late in the drug development process, limiting the number of drug combinations tested. In recent years, there has been significant progress using in vitro, in vivo, and computational methodologies to interrogate combination drug effects. AREAS COVERED This review discusses the advances in these methodologies and how they may be used in conjunction with new successful clinical trials of novel drug combinations to design optimized combination therapies for tuberculosis. Literature searches for approaches and experimental models used to evaluate drug combination effects were undertaken. EXPERT OPINION We are entering an era richer in combination drug effect and pharmacokinetic/pharmacodynamic data, genetic tools, and outcome measurement types. Application of computational modeling approaches that integrate these data and produce predictive models of clinical outcomes may enable the field to generate novel, effective multidrug therapies using existing and new drug combination backbones.
Collapse
Affiliation(s)
- Jonah Larkins-Ford
- Department of Molecular Biology and Microbiology and Tufts University School of Graduate Biomedical Sciences, Tufts University School of Medicine, Boston, MA, USA
- Stuart B. Levy Center for Integrated Management of Antimicrobial Resistance (CIMAR), Tufts University, Boston, MA, USA
- Current address: MarvelBiome Inc, Woburn, MA, USA
| | - Bree B. Aldridge
- Department of Molecular Biology and Microbiology and Tufts University School of Graduate Biomedical Sciences, Tufts University School of Medicine, Boston, MA, USA
- Stuart B. Levy Center for Integrated Management of Antimicrobial Resistance (CIMAR), Tufts University, Boston, MA, USA
- Department of Biomedical Engineering, Tufts University School of Engineering, Medford, MA, USA
| |
Collapse
|
4
|
Millar JA, Butler JR, Evans S, Mattila JT, Linderman JJ, Flynn JL, Kirschner DE. Spatial Organization and Recruitment of Non-Specific T Cells May Limit T Cell-Macrophage Interactions Within Mycobacterium tuberculosis Granulomas. Front Immunol 2021; 11:613638. [PMID: 33552077 PMCID: PMC7855029 DOI: 10.3389/fimmu.2020.613638] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 12/01/2020] [Indexed: 12/23/2022] Open
Abstract
Tuberculosis (TB) is a worldwide health problem; successful interventions such as vaccines and treatment require a 2better understanding of the immune response to infection with Mycobacterium tuberculosis (Mtb). In many infectious diseases, pathogen-specific T cells that are recruited to infection sites are highly responsive and clear infection. Yet in the case of infection with Mtb, most individuals are unable to clear infection leading to either an asymptomatically controlled latent infection (the majority) or active disease (roughly 5%-10% of infections). The hallmark of Mtb infection is the recruitment of immune cells to lungs leading to development of multiple lung granulomas. Non-human primate models of TB indicate that on average <10% of T cells within granulomas are Mtb-responsive in terms of cytokine production. The reason for this reduced responsiveness is unknown and it may be at the core of why humans typically are unable to clear Mtb infection. There are a number of hypotheses as to why this reduced responsiveness may occur, including T cell exhaustion, direct downregulation of antigen presentation by Mtb within infected macrophages, the spatial organization of the granuloma itself, and/or recruitment of non-Mtb-specific T cells to lungs. We use a systems biology approach pairing data and modeling to dissect three of these hypotheses. We find that the structural organization of granulomas as well as recruitment of non-specific T cells likely contribute to reduced responsiveness.
Collapse
Affiliation(s)
- Jess A Millar
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, United States.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - J Russell Butler
- Department of Health and Biomedical Sciences, AdventHealth University, Orlando, FL, United States
| | - Stephanie Evans
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Joshua T Mattila
- Department of Infectious Diseases and Microbiology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Jennifer J Linderman
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - JoAnne L Flynn
- Department of Microbiology and Molecular Genetics and the Center for Vaccine Research, University of Pittsburgh, Pittsburgh, PA, United States
| | - Denise E Kirschner
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, United States
| |
Collapse
|
5
|
Ernest JP, Strydom N, Wang Q, Zhang N, Nuermberger E, Dartois V, Savic RM. Development of New Tuberculosis Drugs: Translation to Regimen Composition for Drug-Sensitive and Multidrug-Resistant Tuberculosis. Annu Rev Pharmacol Toxicol 2020; 61:495-516. [PMID: 32806997 DOI: 10.1146/annurev-pharmtox-030920-011143] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Tuberculosis (TB) kills more people than any other infectious disease. Challenges for developing better treatments include the complex pathology due to within-host immune dynamics, interpatient variability in disease severity and drug pharmacokinetics-pharmacodynamics (PK-PD), and the growing emergence of resistance. Model-informed drug development using quantitative and translational pharmacology has become increasingly recognized as a method capable of drug prioritization and regimen optimization to efficiently progress compounds through TB drug development phases. In this review, we examine translational models and tools, including plasma PK scaling, site-of-disease lesion PK, host-immune and bacteria interplay, combination PK-PD models of multidrug regimens, resistance formation, and integration of data across nonclinical and clinical phases.We propose a workflow that integrates these tools with computational platforms to identify drug combinations that have the potential to accelerate sterilization, reduce relapse rates, and limit the emergence of resistance.
Collapse
Affiliation(s)
- Jacqueline P Ernest
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94158, USA;
| | - Natasha Strydom
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94158, USA;
| | - Qianwen Wang
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94158, USA;
| | - Nan Zhang
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94158, USA;
| | - Eric Nuermberger
- Center for Tuberculosis Research, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA
| | - Véronique Dartois
- Center for Discovery and Innovation, Hackensack Meridian School of Medicine at Seton Hall University, Nutley, New Jersey 07110, USA
| | - Rada M Savic
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94158, USA;
| |
Collapse
|
6
|
Clinical Significance of M1/M2 Macrophages and Related Cytokines in Patients with Spinal Tuberculosis. DISEASE MARKERS 2020; 2020:2509454. [PMID: 32566036 PMCID: PMC7267866 DOI: 10.1155/2020/2509454] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 04/22/2020] [Indexed: 11/24/2022]
Abstract
Background Macrophages are important immune cells involved in Mycobacterium tuberculosis (M.tb) infection. To further investigate the degree of disease development in patients with spinal tuberculosis (TB), we conducted research on macrophage polarization. Methods Thirty-six patients with spinal TB and twenty-five healthy controls were enrolled in this study. The specific morphology of tuberculous granuloma in spinal tissue was observed by hematoxylin-eosin (H&E) staining. The presence and distribution of bacilli were observed by Ziehl-Neelsen (ZN) staining. Macrophage-specific molecule CD68 was detected by immunohistochemistry (IHC). M1 macrophages play a proinflammatory role, including the specific molecule nitric oxide synthase (iNOS) and the related cytokine tumor necrosis factor-α (TNF-α) and interferon-γ (IFN-γ). M2 macrophages exert anti-inflammatory effects, including the specific molecule CD163 and related cytokine interleukin-10 (IL-10). The above markers were all detected by quantitative real-time PCR (RT-PCR), enzyme-linked immunosorbent assay (ELISA), and IHC. Results Typical tuberculous granuloma was observed in the HE staining of patients with spinal TB. ZN staining showed positive expression of Ag85B around the caseous necrosis tissue and Langerhans multinucleated giant cells. At the same time, IHC results indicated that CD68, iNOS, CD163, IL-10, TNF-α, and IFN-γ were expressed around the tuberculous granuloma, and their levels were obviously higher in close tissue than in the distant tissue. RT-PCR and ELISA results indicated that IL-10, TNF-α, and IFN-γ levels of TB patients were also higher than those of the healthy controls. Conclusion The report here highlights that two types of macrophage polarization (M1 and M2) are present in the tissues and peripheral blood of patients with spinal TB. Macrophages also play proinflammatory and anti-inflammatory roles. Macrophage polarization is involved in spinal TB infection.
Collapse
|
7
|
Leonard-Duke J, Evans S, Hannan RT, Barker TH, Bates JHT, Bonham CA, Moore BB, Kirschner DE, Peirce SM. Multi-scale models of lung fibrosis. Matrix Biol 2020; 91-92:35-50. [PMID: 32438056 DOI: 10.1016/j.matbio.2020.04.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 03/13/2020] [Accepted: 04/15/2020] [Indexed: 02/08/2023]
Abstract
The architectural complexity of the lung is crucial to its ability to function as an organ of gas exchange; the branching tree structure of the airways transforms the tracheal cross-section of only a few square centimeters to a blood-gas barrier with a surface area of tens of square meters and a thickness on the order of a micron or less. Connective tissue comprised largely of collagen and elastic fibers provides structural integrity for this intricate and delicate system. Homeostatic maintenance of this connective tissue, via a balance between catabolic and anabolic enzyme-driven processes, is crucial to life. Accordingly, when homeostasis is disrupted by the excessive production of connective tissue, lung function deteriorates rapidly with grave consequences leading to chronic lung conditions such as pulmonary fibrosis. Understanding how pulmonary fibrosis develops and alters the link between lung structure and function is crucial for diagnosis, prognosis, and therapy. Further information gained could help elaborate how the healing process breaks down leading to chronic disease. Our understanding of fibrotic disease is greatly aided by the intersection of wet lab studies and mathematical and computational modeling. In the present review we will discuss how multi-scale modeling has facilitated our understanding of pulmonary fibrotic disease as well as identified opportunities that remain open and have produced techniques that can be incorporated into this field by borrowing approaches from multi-scale models of fibrosis beyond the lung.
Collapse
Affiliation(s)
- Julie Leonard-Duke
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Stephanie Evans
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Riley T Hannan
- Department of Pathology, University of Virginia, Charlottesville, VA 22908, USA
| | - Thomas H Barker
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Jason H T Bates
- Department of Medicine, Vermont Lung Center, University of Vermont College of Medicine, Burlington, VT 05405, USA
| | - Catherine A Bonham
- Division of Pulmonary and Critical Care Medicine, University of Virginia, Charlottesville VA 22908, USA
| | - Bethany B Moore
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, and Department of Microbiology and Immunology, University of Michigan Medical Center, Ann Arbor, MI, 48109, USA
| | - Denise E Kirschner
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Shayn M Peirce
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA; Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA 22908, USA.
| |
Collapse
|
8
|
Renardy M, Hult C, Evans S, Linderman JJ, Kirschner DE. Global sensitivity analysis of biological multi-scale models. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2019; 11:109-116. [PMID: 32864523 DOI: 10.1016/j.cobme.2019.09.012] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Mathematical models of biological systems need to both reflect and manage the inherent complexities of biological phenomena. Through their versatility and ability to capture behavior at multiple scales, multi-scale models offer a valuable approach. Due to the typically nonlinear and stochastic nature of multi-scale models as well as unknown parameter values, various types of uncertainty are present; thus, effective assessment and quantification of such uncertainty through sensitivity analysis is important. In this review, we discuss global sensitivity analysis in the context of multi-scale and multi-compartment models and highlight its value in model development and analysis. We present an overview of sensitivity analysis methods, approaches for extending such methods to a multi-scale setting, and examples of how sensitivity analysis can inform model reduction. Through schematics and references to past work, we aim to emphasize the advantages and usefulness of such techniques.
Collapse
Affiliation(s)
- Marissa Renardy
- University of Michigan Medical School, Department of Microbiology and Immunology
| | - Caitlin Hult
- University of Michigan Medical School, Department of Microbiology and Immunology
- University of Michigan, Department of Chemical Engineering
| | - Stephanie Evans
- University of Michigan Medical School, Department of Microbiology and Immunology
| | | | - Denise E Kirschner
- University of Michigan Medical School, Department of Microbiology and Immunology
| |
Collapse
|
9
|
Renardy M, Wessler T, Blemker S, Linderman J, Peirce S, Kirschner D. Data-Driven Model Validation Across Dimensions. Bull Math Biol 2019; 81:1853-1866. [PMID: 30830675 PMCID: PMC6494696 DOI: 10.1007/s11538-019-00590-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Accepted: 02/21/2019] [Indexed: 10/27/2022]
Abstract
Data-driven model validation across dimensions in mathematical and computational biology assumptions are often made (e.g., symmetry) to reduce the problem from three spatial dimensions (3D) to two (2D). However, some experimental datasets, such as cell counts obtained via flow cytometry, represent the entire 3D biological object. For purpose of model calibration and validation, it is sometimes necessary to compare these biological datasets with model outputs. We propose a methodology for scaling 2D model outputs to compare with 3D experimental datasets, and we discuss the application of this methodology to two examples: agent-based models of granuloma formation and skeletal muscle tissue. The accuracy of the method is evaluated in artificially generated scenarios.
Collapse
Affiliation(s)
- Marissa Renardy
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA
| | - Timothy Wessler
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Silvia Blemker
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Jennifer Linderman
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Shayn Peirce
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Denise Kirschner
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA.
| |
Collapse
|