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Geng S, Pradhan K, Li L. Signal-Strength and History-Dependent Innate Immune Memory Dynamics in Health and Disease. Handb Exp Pharmacol 2022; 276:23-41. [PMID: 34085119 DOI: 10.1007/164_2021_485] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Innate immunity exhibits memory characteristics, reflected not only in selective recognition of external microbial or internal damage signals, but more importantly in history and signal-strength dependent reprogramming of innate leukocytes characterized by priming, tolerance, and exhaustion. Key innate immune cells such as monocytes and neutrophils can finely discern and attune to the duration and intensity of external signals through rewiring of internal signaling circuitries, giving rise to a vast array of discreet memory phenotypes critically relevant to managing tissue homeostasis as well as diverse repertoires of inflammatory conditions. This review will highlight recent advances in this rapidly expanding field of innate immune programming and memory, as well as its translational implication in the pathophysiology of selected inflammatory diseases.
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Affiliation(s)
- Shuo Geng
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA
| | - Kisha Pradhan
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA
| | - Liwu Li
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA.
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Mishra D, Bepler T, Teague B, Berger B, Broach J, Weiss R. An engineered protein-phosphorylation toggle network with implications for endogenous network discovery. Science 2021; 373:eaav0780. [PMID: 34210851 PMCID: PMC11203391 DOI: 10.1126/science.aav0780] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Accepted: 04/29/2021] [Indexed: 12/23/2022]
Abstract
Synthetic biological networks comprising fast, reversible reactions could enable engineering of new cellular behaviors that are not possible with slower regulation. Here, we created a bistable toggle switch in Saccharomyces cerevisiae using a cross-repression topology comprising 11 protein-protein phosphorylation elements. The toggle is ultrasensitive, can be induced to switch states in seconds, and exhibits long-term bistability. Motivated by our toggle's architecture and size, we developed a computational framework to search endogenous protein pathways for other large and similar bistable networks. Our framework helped us to identify and experimentally verify five formerly unreported endogenous networks that exhibit bistability. Building synthetic protein-protein networks will enable bioengineers to design fast sensing and processing systems, allow sophisticated regulation of cellular processes, and aid discovery of endogenous networks with particular functions.
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Affiliation(s)
- Deepak Mishra
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tristan Bepler
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Computational and Systems Biology Graduate Program, Massachusetts Institute of Technology, Cambridge, MA, USA
- Simons Machine Learning Center, New York Structural Biology Center, New York, NY, USA
| | - Brian Teague
- Department of Biology, University of Wisconsin, Stout, WI, USA
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Computational and Systems Biology Graduate Program, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jim Broach
- Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, PA, USA
| | - Ron Weiss
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Computational and Systems Biology Graduate Program, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
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Ciupe SM, Boribong BP, Kadelka S, Jones CN. Bistable Mathematical Model of Neutrophil Migratory Patterns After LPS-Induced Epigenetic Reprogramming. Front Genet 2021; 12:633963. [PMID: 33708241 PMCID: PMC7940759 DOI: 10.3389/fgene.2021.633963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 01/27/2021] [Indexed: 11/17/2022] Open
Abstract
The highly controlled migration of neutrophils toward the site of an infection can be altered when they are trained with lipopolysaccharides (LPS), with high dose LPS enhancing neutrophil migratory pattern toward the bacterial derived source signal and super-low dose LPS inducing either migration toward an intermediary signal or dysregulation and oscillatory movement. Empirical studies that use microfluidic chemotaxis-chip devices with two opposing chemoattractants showed differential neutrophil migration after challenge with different LPS doses. The epigenetic alterations responsible for changes in neutrophil migratory behavior are unknown. We developed two mathematical models that evaluate the mechanistic interactions responsible for neutrophil migratory decision-making when exposed to competing chemoattractants and challenged with LPS. The first model, which considers the interactions between the receptor densities of two competing chemoattractants, their kinases, and LPS, displayed bistability between high and low ratios of primary to intermediary chemoattractant receptor densities. In particular, at equilibrium, we observe equal receptor densities for low LPS (< 15ng/mL); and dominance of receptors for the primary chemoattractant for high LPS (> 15ng/mL). The second model, which included additional interactions with an extracellular signal-regulated kinase in both phosphorylated and non-phosphorylated forms, has an additional dynamic outcome, oscillatory dynamics for both receptors, as seen in the data. In particular, it found equal receptor densities in the absence of oscillation for super-low and high LPS challenge (< 0.4 and 1.1 376 ng/mL). Predicting the mechanisms and the type of external LPS challenge responsible for neutrophils migration toward pro-inflammatory chemoattractants, migration toward pro-tolerant chemoattractants, or oscillatory movement is necessary knowledge in designing interventions against immune diseases, such as sepsis.
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Affiliation(s)
- Stanca M. Ciupe
- Department of Mathematics, Virginia Tech, Blacksburg, VA, United States
| | - Brittany P. Boribong
- Division of Pediatric Pulmonology, Massachusetts General Hospital, Boston, MA, United States
| | - Sarah Kadelka
- Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
| | - Caroline N. Jones
- Department of Bioengineering, University of Texas, Dallas, TX, United States
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Weinstock LD, Forsmo JE, Wilkinson A, Ueda J, Wood LB. Experimental Control of Macrophage Pro-Inflammatory Dynamics Using Predictive Models. Front Bioeng Biotechnol 2020; 8:666. [PMID: 32766211 PMCID: PMC7381235 DOI: 10.3389/fbioe.2020.00666] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Accepted: 05/28/2020] [Indexed: 12/31/2022] Open
Abstract
Macrophage activity is a major component of the healthy response to infection and injury that consists of tightly regulated early pro-inflammatory activation followed by anti-inflammatory and regenerative activity. In numerous diseases, however, macrophage polarization becomes dysregulated and can not only impair recovery, but can promote further injury and pathogenesis, e.g., after trauma or in diabetic ulcers. Dysregulated macrophages may either fail to polarize or become chronically polarized, resulting in increased production of cytotoxic factors, diminished capacity to clear pathogens, or failure to promote tissue regeneration. In these cases, a method of predicting and dynamically controlling macrophage polarization will enable a new strategy for treating diverse inflammatory diseases. In this work, we developed a model-predictive control framework to temporally regulate macrophage polarization. Using RAW 264.7 macrophages as a model system, we enabled temporal control by identifying transfer function models relating the polarization marker iNOS to exogenous pro- and anti-inflammatory stimuli. These stimuli-to-iNOS response models were identified using linear autoregressive with exogenous input terms (ARX) equations and were coupled with non-linear elements to account for experimentally identified supra-additive and hysteretic effects. Using this model architecture, we were able to reproduce experimentally observed temporal iNOS dynamics induced by lipopolysaccharides (LPS) and interferon gamma (IFN-γ). Moreover, the identified model enabled the design of time-varying input trajectories to experimentally sustain the duration and magnitude of iNOS expression. By designing transfer function models with the intent to predict cell behavior, we were able to predict and experimentally obtain temporal regulation of iNOS expression using LPS and IFN-γ from both naïve and non-naïve initial states. Moreover, our data driven models revealed decaying magnitude of iNOS response to LPS stimulation over time that could be recovered using combined treatment with both LPS and IFN-γ. Given the importance of dynamic tissue macrophage polarization and overall inflammatory regulation to a broad number of diseases, the temporal control methodology presented here will have numerous applications for regulating immune activity dynamics in chronic inflammatory diseases.
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Affiliation(s)
- Laura D. Weinstock
- Parker H. Petit Institute for Bioengineering & Bioscience, Georgia Institute of Technology, Atlanta, GA, United States
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - James E. Forsmo
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Alexis Wilkinson
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Jun Ueda
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Levi B. Wood
- Parker H. Petit Institute for Bioengineering & Bioscience, Georgia Institute of Technology, Atlanta, GA, United States
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
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