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Plaugher D, Murrugarra D. Pancreatic cancer mutationscape: revealing the link between modular restructuring and intervention efficacy amidst common mutations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.27.577546. [PMID: 38352601 PMCID: PMC10862704 DOI: 10.1101/2024.01.27.577546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/04/2024]
Abstract
There is increasing evidence that biological systems are modular in both structure and function. Complex biological signaling networks such as gene regulatory networks (GRNs) are proving to be composed of subcategories that are interconnected and hierarchically ranked. These networks contain highly dynamic processes that ultimately dictate cellular function over time, as well as influence phenotypic fate transitions. In this work, we use a stochastic multicellular signaling network of pancreatic cancer (PC) to show that the variance in topological rankings of the most phenotypically influential modules implies a strong relationship between structure and function. We further show that induction of mutations alters the modular structure, which analogously influences the aggression and controllability of the disease in silico. We finally present evidence that the impact and location of mutations with respect to PC modular structure directly corresponds to the efficacy of single agent treatments in silico, because topologically deep mutations require deep targets for control.
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Affiliation(s)
- Daniel Plaugher
- Department of Toxicology and Cancer Biology, University of Kentucky
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2
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Frank ASJ, Larripa K, Ryu H, Röblitz S. Macrophage phenotype transitions in a stochastic gene-regulatory network model. J Theor Biol 2023; 575:111634. [PMID: 37839584 DOI: 10.1016/j.jtbi.2023.111634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 08/11/2023] [Accepted: 10/05/2023] [Indexed: 10/17/2023]
Abstract
Polarization is the process by which a macrophage cell commits to a phenotype based on external signal stimulation. To know how this process is affected by random fluctuations and events within a cell is of utmost importance to better understand the underlying dynamics and predict possible phenotype transitions. For this purpose, we develop a stochastic modeling approach for the macrophage polarization process. We classify phenotype states using the Robust Perron Cluster Analysis and quantify transition pathways and probabilities by applying Transition Path Theory. Depending on the model parameters, we identify four bistable and one tristable phenotype configuration. We find that bistable transitions are fast but their states less robust. In contrast, phenotype transitions in the tristable situation have a comparatively long time duration, which reflects the robustness of the states. The results indicate parallels in the overall transition behavior of macrophage cells with other heterogeneous and plastic cell types, such as cancer cells. Our approach allows for a probabilistic interpretation of macrophage phenotype transitions and biological inference on phenotype robustness. In general, the methodology can easily be adapted to other systems where random state switches are known to occur.
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Affiliation(s)
| | - Kamila Larripa
- Department of Mathematics, California State Polytechnic University Humboldt, Arcata, CA, USA.
| | - Hwayeon Ryu
- Department of Mathematics and Statistics, Elon University, Elon, NC, USA.
| | - Susanna Röblitz
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.
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3
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Uleman JF, Mancini E, Al-Shama RF, te Velde AA, Kraneveld AD, Castiglione F. A multiscale hybrid model for exploring the effect of Resolvin D1 on macrophage polarization during acute inflammation. Math Biosci 2023; 359:108997. [PMID: 36996999 DOI: 10.1016/j.mbs.2023.108997] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 03/23/2023] [Accepted: 03/24/2023] [Indexed: 03/31/2023]
Abstract
Dysregulated inflammation underlies various diseases. Specialized pro-resolving mediators (SPMs) like Resolvin D1 (RvD1) have been shown to resolve inflammation and halt disease progression. Macrophages, key immune cells that drive inflammation, respond to the presence of RvD1 by polarizing to an anti-inflammatory type (M2). However, RvD1's mechanisms, roles, and utility are not fully understood. This paper introduces a gene-regulatory network (GRN) model that contains pathways for RvD1 and other SPMs and proinflammatory molecules like lipopolysaccharides. We couple this GRN model to a partial differential equation - agent-based hybrid model using a multiscale framework to simulate an acute inflammatory response with and without the presence of RvD1. We calibrate and validate the model using experimental data from two animal models. The model reproduces the dynamics of key immune components and the effects of RvD1 during acute inflammation. Our results suggest RvD1 can drive macrophage polarization through the G protein-coupled receptor 32 (GRP32) pathway. The presence of RvD1 leads to an earlier and increased M2 polarization, reduced neutrophil recruitment, and faster apoptotic neutrophil clearance. These results support a body of literature that suggests that RvD1 is a promising candidate for promoting the resolution of acute inflammation. We conclude that once calibrated and validated on human data, the model can identify critical sources of uncertainty, which could be further elucidated in biological experiments and assessed for clinical use.
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Gonçalves LO, Pulido AFV, Mathias FAS, Enes AES, Carvalho MGR, de Melo Resende D, Polak ME, Ruiz JC. Expression Profile of Genes Related to the Th17 Pathway in Macrophages Infected by Leishmania major and Leishmania amazonensis: The Use of Gene Regulatory Networks in Modeling This Pathway. Front Cell Infect Microbiol 2022; 12:826523. [PMID: 35774406 PMCID: PMC9239034 DOI: 10.3389/fcimb.2022.826523] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 03/09/2022] [Indexed: 11/13/2022] Open
Abstract
Leishmania amazonensis and Leishmania major are the causative agents of cutaneous and mucocutaneous diseases. The infections‘ outcome depends on host–parasite interactions and Th1/Th2 response, and in cutaneous form, regulation of Th17 cytokines has been reported to maintain inflammation in lesions. Despite that, the Th17 regulatory scenario remains unclear. With the aim to gain a better understanding of the transcription factors (TFs) and genes involved in Th17 induction, in this study, the role of inducing factors of the Th17 pathway in Leishmania–macrophage infection was addressed through computational modeling of gene regulatory networks (GRNs). The Th17 GRN modeling integrated experimentally validated data available in the literature and gene expression data from a time-series RNA-seq experiment (4, 24, 48, and 72 h post-infection). The generated model comprises a total of 10 TFs, 22 coding genes, and 16 cytokines related to the Th17 immune modulation. Addressing the Th17 induction in infected and uninfected macrophages, an increase of 2- to 3-fold in 4–24 h was observed in the former. However, there was a decrease in basal levels at 48–72 h for both groups. In order to evaluate the possible outcomes triggered by GRN component modulation in the Th17 pathway. The generated GRN models promoted an integrative and dynamic view of Leishmania–macrophage interaction over time that extends beyond the analysis of single-gene expression.
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Affiliation(s)
- Leilane Oliveira Gonçalves
- Programa de Pós-graduação em Biologia Computacional e Sistemas, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil
- Grupo Informática de Biossistemas, Instituto René Rachou, Fiocruz Minas, Belo Horizonte, Brazil
| | - Andrés F. Vallejo Pulido
- Systems Immunology Group, Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
| | | | - Alexandre Estevão Silvério Enes
- Programa de Pós-graduação em Biologia Computacional e Sistemas, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil
- Grupo Informática de Biossistemas, Instituto René Rachou, Fiocruz Minas, Belo Horizonte, Brazil
| | | | - Daniela de Melo Resende
- Grupo Genômica Funcional de Parasitos, Instituto René Rachou, Fiocruz Minas, Belo Horizonte, Brazil
| | - Marta E. Polak
- Systems Immunology Group, Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- *Correspondence: Jeronimo C. Ruiz, ; Marta E. Polak,
| | - Jeronimo C. Ruiz
- Grupo Informática de Biossistemas, Instituto René Rachou, Fiocruz Minas, Belo Horizonte, Brazil
- *Correspondence: Jeronimo C. Ruiz, ; Marta E. Polak,
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Castiglione F, Deb D, Srivastava AP, Liò P, Liso A. From Infection to Immunity: Understanding the Response to SARS-CoV2 Through In-Silico Modeling. Front Immunol 2021; 12:646972. [PMID: 34557181 PMCID: PMC8453017 DOI: 10.3389/fimmu.2021.646972] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 08/09/2021] [Indexed: 12/23/2022] Open
Abstract
Background Immune system conditions of the patient is a key factor in COVID-19 infection survival. A growing number of studies have focused on immunological determinants to develop better biomarkers for therapies. Aim Studies of the insurgence of immunity is at the core of both SARS-CoV-2 vaccine development and therapies. This paper attempts to describe the insurgence (and the span) of immunity in COVID-19 at the population level by developing an in-silico model. We simulate the immune response to SARS-CoV-2 and analyze the impact of infecting viral load, affinity to the ACE2 receptor, and age in an artificially infected population on the course of the disease. Methods We use a stochastic agent-based immune simulation platform to construct a virtual cohort of infected individuals with age-dependent varying degrees of immune competence. We use a parameter set to reproduce known inter-patient variability and general epidemiological statistics. Results By assuming the viremia at day 30 of the infection to be the proxy for lethality, we reproduce in-silico several clinical observations and identify critical factors in the statistical evolution of the infection. In particular, we evidence the importance of the humoral response over the cytotoxic response and find that the antibody titers measured after day 25 from the infection are a prognostic factor for determining the clinical outcome of the infection. Our modeling framework uses COVID-19 infection to demonstrate the actionable effectiveness of modeling the immune response at individual and population levels. The model developed can explain and interpret observed patterns of infection and makes verifiable temporal predictions. Within the limitations imposed by the simulated environment, this work proposes quantitatively that the great variability observed in the patient outcomes in real life can be the mere result of subtle variability in the infecting viral load and immune competence in the population. In this work, we exemplify how computational modeling of immune response provides an important view to discuss hypothesis and design new experiments, in particular paving the way to further investigations about the duration of vaccine-elicited immunity especially in the view of the blundering effect of immunosenescence.
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Affiliation(s)
- Filippo Castiglione
- Institute for Applied Computing (IAC), National Research Council of Italy (CNR), Rome, Italy
| | - Debashrito Deb
- Department of Biochemistry, School of Applied Sciences, REVA University, Bangalore, India
| | | | - Pietro Liò
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Arcangelo Liso
- Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
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7
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Stolfi P, Valentini I, Palumbo MC, Tieri P, Grignolio A, Castiglione F. Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices. BMC Bioinformatics 2020; 21:508. [PMID: 33308172 PMCID: PMC7733701 DOI: 10.1186/s12859-020-03763-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 09/17/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND The aim of a recent research project was the investigation of the mechanisms involved in the onset of type 2 diabetes in the absence of familiarity. This has led to the development of a computational model that recapitulates the aetiology of the disease and simulates the immunological and metabolic alterations linked to type-2 diabetes subjected to clinical, physiological, and behavioural features of prototypical human individuals. RESULTS We analysed the time course of 46,170 virtual subjects, experiencing different lifestyle conditions. We then set up a statistical model able to recapitulate the simulated outcomes. CONCLUSIONS The resulting machine learning model adequately predicts the synthetic dataset and can, therefore, be used as a computationally-cheaper version of the detailed mathematical model, ready to be implemented on mobile devices to allow self-assessment by informed and aware individuals. The computational model used to generate the dataset of this work is available as a web-service at the following address: http://kraken.iac.rm.cnr.it/T2DM .
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Affiliation(s)
- Paola Stolfi
- Institute for Applied Mathematics, National Research Council of Italy, Rome, Italy
| | | | | | - Paolo Tieri
- Institute for Applied Mathematics, National Research Council of Italy, Rome, Italy
| | - Andrea Grignolio
- Research Ethics and Integrity Interdepartmental Center, National Research Council of Italy, Rome, Italy
- Medical Humanities - International MD Program, Vita-Salute San Raffaele University, Milan, Italy
| | - Filippo Castiglione
- Institute for Applied Mathematics, National Research Council of Italy, Rome, Italy
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8
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Frank AS, Larripa K, Ryu H, Snodgrass RG, Röblitz S. Bifurcation and sensitivity analysis reveal key drivers of multistability in a model of macrophage polarization. J Theor Biol 2020; 509:110511. [PMID: 33045246 DOI: 10.1016/j.jtbi.2020.110511] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 09/24/2020] [Accepted: 10/01/2020] [Indexed: 12/13/2022]
Abstract
In this paper, we present and analyze a mathematical model for polarization of a single macrophage which, despite its simplicity, exhibits complex dynamics in terms of multistability. In particular, we demonstrate that an asymmetry in the regulatory mechanisms and parameter values is important for observing multiple phenotypes. Bifurcation and sensitivity analyses show that external signaling cues are necessary for macrophage commitment and emergence to a phenotype, but that the intrinsic macrophage pathways are equally important. Based on our numerical results, we formulate hypotheses that could be further investigated by laboratory experiments to deepen our understanding of macrophage polarization.
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Affiliation(s)
- Anna S Frank
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.
| | - Kamila Larripa
- Department of Mathematics, Humboldt State University, Arcata, CA, USA.
| | - Hwayeon Ryu
- Department of Mathematics and Statistics, Elon University, Elon, NC, USA.
| | - Ryan G Snodgrass
- Institute of Biochemistry, Goethe-University, Frankfurt, Germany.
| | - Susanna Röblitz
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.
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9
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Aguilar B, Gibbs DL, Reiss DJ, McConnell M, Danziger SA, Dervan A, Trotter M, Bassett D, Hershberg R, Ratushny AV, Shmulevich I. A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma. Gigascience 2020; 9:giaa075. [PMID: 32696951 PMCID: PMC7374045 DOI: 10.1093/gigascience/giaa075] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 02/14/2020] [Accepted: 06/21/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Mechanistic models, when combined with pertinent data, can improve our knowledge regarding important molecular and cellular mechanisms found in cancer. These models make the prediction of tissue-level response to drug treatment possible, which can lead to new therapies and improved patient outcomes. Here we present a data-driven multiscale modeling framework to study molecular interactions between cancer, stromal, and immune cells found in the tumor microenvironment. We also develop methods to use molecular data available in The Cancer Genome Atlas to generate sample-specific models of cancer. RESULTS By combining published models of different cells relevant to pancreatic ductal adenocarcinoma (PDAC), we built an agent-based model of the multicellular pancreatic tumor microenvironment, formally describing cell type-specific molecular interactions and cytokine-mediated cell-cell communications. We used an ensemble-based modeling approach to systematically explore how variations in the tumor microenvironment affect the viability of cancer cells. The results suggest that the autocrine loop involving EGF signaling is a key interaction modulator between pancreatic cancer and stellate cells. EGF is also found to be associated with previously described subtypes of PDAC. Moreover, the model allows a systematic exploration of the effect of possible therapeutic perturbations; our simulations suggest that reducing bFGF secretion by stellate cells will have, on average, a positive impact on cancer apoptosis. CONCLUSIONS The developed framework allows model-driven hypotheses to be generated regarding therapeutically relevant PDAC states with potential molecular and cellular drivers indicating specific intervention strategies.
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Affiliation(s)
- Boris Aguilar
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA 98109, USA
| | - David L Gibbs
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA 98109, USA
| | - David J Reiss
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Mark McConnell
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Samuel A Danziger
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Andrew Dervan
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Matthew Trotter
- BMS Center for Innovation and Translational Research Europe (CITRE), Pabellon de Italia, Calle Isaac Newton 4, Sevilla 41092, Spain
| | - Douglas Bassett
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Robert Hershberg
- Formerly Celgene Corporation, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Alexander V Ratushny
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Ilya Shmulevich
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA 98109, USA
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10
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Maturo MG, Soligo M, Gibson G, Manni L, Nardini C. The greater inflammatory pathway-high clinical potential by innovative predictive, preventive, and personalized medical approach. EPMA J 2020; 11:1-16. [PMID: 32140182 PMCID: PMC7028895 DOI: 10.1007/s13167-019-00195-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 11/13/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND AND LIMITATIONS Impaired wound healing (WH) and chronic inflammation are hallmarks of non-communicable diseases (NCDs). However, despite WH being a recognized player in NCDs, mainstream therapies focus on (un)targeted damping of the inflammatory response, leaving WH largely unaddressed, owing to three main factors. The first is the complexity of the pathway that links inflammation and wound healing; the second is the dual nature, local and systemic, of WH; and the third is the limited acknowledgement of genetic and contingent causes that disrupt physiologic progression of WH. PROPOSED APPROACH Here, in the frame of Predictive, Preventive, and Personalized Medicine (PPPM), we integrate and revisit current literature to offer a novel systemic view on the cues that can impact on the fate (acute or chronic inflammation) of WH, beyond the compartmentalization of medical disciplines and with the support of advanced computational biology. CONCLUSIONS This shall open to a broader understanding of the causes for WH going awry, offering new operational criteria for patients' stratification (prediction and personalization). While this may also offer improved options for targeted prevention, we will envisage new therapeutic strategies to reboot and/or boost WH, to enable its progression across its physiological phases, the first of which is a transient acute inflammatory response versus the chronic low-grade inflammation characteristic of NCDs.
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Affiliation(s)
- Maria Giovanna Maturo
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, L’Aquila, Italy
| | - Marzia Soligo
- Institute of Translational Pharmacology, Consiglio Nazionale delle Ricerche (CNR), Rome, Italy
| | - Greg Gibson
- Center for Integrative Genomics, School of Biological Sciences, Georgia Tech, Atlanta, GA USA
| | - Luigi Manni
- Institute of Translational Pharmacology, Consiglio Nazionale delle Ricerche (CNR), Rome, Italy
| | - Christine Nardini
- IAC Institute for Applied Computing, Consiglio Nazionale delle Ricerche (CNR), Rome, Italy
- Bio Unit, Scientific and Medical Direction, SOL Group, Monza, Italy
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Zhao C, Mirando AC, Sové RJ, Medeiros TX, Annex BH, Popel AS. A mechanistic integrative computational model of macrophage polarization: Implications in human pathophysiology. PLoS Comput Biol 2019; 15:e1007468. [PMID: 31738746 PMCID: PMC6860420 DOI: 10.1371/journal.pcbi.1007468] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 10/08/2019] [Indexed: 12/24/2022] Open
Abstract
Macrophages respond to signals in the microenvironment by changing their functional phenotypes, a process known as polarization. Depending on the context, they acquire different patterns of transcriptional activation, cytokine expression and cellular metabolism which collectively constitute a continuous spectrum of phenotypes, of which the two extremes are denoted as classical (M1) and alternative (M2) activation. To quantitatively decode the underlying principles governing macrophage phenotypic polarization and thereby harness its therapeutic potential in human diseases, a systems-level approach is needed given the multitude of signaling pathways and intracellular regulation involved. Here we develop the first mechanism-based, multi-pathway computational model that describes the integrated signal transduction and macrophage programming under M1 (IFN-γ), M2 (IL-4) and cell stress (hypoxia) stimulation. Our model was calibrated extensively against experimental data, and we mechanistically elucidated several signature feedbacks behind the M1-M2 antagonism and investigated the dynamical shaping of macrophage phenotypes within the M1-M2 spectrum. Model sensitivity analysis also revealed key molecular nodes and interactions as targets with potential therapeutic values for the pathophysiology of peripheral arterial disease and cancer. Through simulations that dynamically capture the signal integration and phenotypic marker expression in the differential macrophage polarization responses, our model provides an important computational basis toward a more quantitative and network-centric understanding of the complex physiology and versatile functions of macrophages in human diseases.
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Affiliation(s)
- Chen Zhao
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- * E-mail:
| | - Adam C. Mirando
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Richard J. Sové
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Thalyta X. Medeiros
- Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, United States of America
- Divison of Cardiovascular Medicine, Department of Medicine, University of Virginia, Charlottesville, Virginia, United States of America
| | - Brian H. Annex
- Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, United States of America
- Divison of Cardiovascular Medicine, Department of Medicine, University of Virginia, Charlottesville, Virginia, United States of America
| | - Aleksander S. Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
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Grebennikov DS, Donets DO, Orlova OG, Argilaguet J, Meyerhans A, Bocharov GA. Mathematical Modeling of the Intracellular Regulation of Immune Processes. Mol Biol 2019. [DOI: 10.1134/s002689331905008x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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13
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Modeling the Effect of High Calorie Diet on the Interplay between Adipose Tissue, Inflammation, and Diabetes. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2019; 2019:7525834. [PMID: 30863457 PMCID: PMC6378014 DOI: 10.1155/2019/7525834] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2018] [Accepted: 01/06/2019] [Indexed: 11/17/2022]
Abstract
Background Type 2 diabetes (T2D) is a chronic metabolic disease potentially leading to serious widespread tissue damage. Human organism develops T2D when the glucose-insulin control is broken for reasons that are not fully understood but have been demonstrated to be linked to the emergence of a chronic inflammation. Indeed such low-level chronic inflammation affects the pancreatic production of insulin and triggers the development of insulin resistance, eventually leading to an impaired control of the blood glucose concentration. On the contrary, it is well-known that obesity and inflammation are strongly correlated. Aim In this study, we investigate in silico the effect of overfeeding on the adipose tissue and the consequent set up of an inflammatory state. We model the emergence of the inflammation as the result of adipose mass increase which, in turn, is a direct consequence of a prolonged excess of high calorie intake. Results The model reproduces the fat accumulation due to excessive caloric intake observed in two clinical studies. Moreover, while showing consistent weight gains over long periods of time, it reveals a drift of the macrophage population toward the proinflammatory phenotype, thus confirming its association with fatness.
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14
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Palma A, Jarrah AS, Tieri P, Cesareni G, Castiglione F. Gene Regulatory Network Modeling of Macrophage Differentiation Corroborates the Continuum Hypothesis of Polarization States. Front Physiol 2018; 9:1659. [PMID: 30546316 PMCID: PMC6278720 DOI: 10.3389/fphys.2018.01659] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Accepted: 11/02/2018] [Indexed: 01/22/2023] Open
Abstract
Macrophages derived from monocyte precursors undergo specific polarization processes which are influenced by the local tissue environment: classically activated (M1) macrophages, with a pro-inflammatory activity and a role of effector cells in Th1 cellular immune responses, and alternatively activated (M2) macrophages, with anti-inflammatory functions and involved in immunosuppression and tissue repair. At least three different subsets of M2 macrophages, namely, M2a, M2b, and M2c, are characterized in the literature based on their eliciting signals. The activation and polarization of macrophages is achieved through many, often intertwined, signaling pathways. To describe the logical relationships among the genes involved in macrophage polarization, we used a computational modeling methodology, namely, logical (Boolean) modeling of gene regulation. We integrated experimental data and knowledge available in the literature to construct a logical network model for the gene regulation driving macrophage polarization to the M1, M2a, M2b, and M2c phenotypes. Using the software GINsim and BoolNet, we analyzed the network dynamics under different conditions and perturbations to understand how they affect cell polarization. Dynamic simulations of the network model, enacting the most relevant biological conditions, showed coherence with the observed behavior of in vivo macrophages. The model could correctly reproduce the polarization toward the four main phenotypes as well as to several hybrid phenotypes, which are known to be experimentally associated to physiological and pathological conditions. We surmise that shifts among different phenotypes in the model mimic the hypothetical continuum of macrophage polarization, with M1 and M2 being the extremes of an uninterrupted sequence of states. Furthermore, model simulations suggest that anti-inflammatory macrophages are resilient to shift back to the pro-inflammatory phenotype.
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Affiliation(s)
- Alessandro Palma
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Abdul Salam Jarrah
- Department of Mathematics and Statistics, American University of Sharjah, Sharjah, United Arab Emirates
| | - Paolo Tieri
- Institute for Applied Computing, National Research Council of Italy, Rome, Italy.,Data Science Program, Sapienza University of Rome, Rome, Italy
| | - Gianni Cesareni
- Department of Biology, University of Rome Tor Vergata, Rome, Italy.,Fondazione Santa Lucia Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Filippo Castiglione
- Institute for Applied Computing, National Research Council of Italy, Rome, Italy
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15
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Asano Y. Recent advances in the treatment of skin involvement in systemic sclerosis. Inflamm Regen 2017; 37:12. [PMID: 29259711 PMCID: PMC5725888 DOI: 10.1186/s41232-017-0047-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Accepted: 04/07/2017] [Indexed: 02/04/2023] Open
Abstract
Skin fibrosis is a devastating clinical condition commonly seen in skin-restricted and systemic disorders. The goal of skin fibrosis treatment is the restoration of abnormally activated dermal fibroblasts producing the excessive amount of extracellular matrix, which is generally a final consequence of the complex disease process including the activation of vascular and immune systems. Among various skin fibrotic conditions, the molecular mechanisms underlying dermal fibroblast activation have been mostly well studied in systemic sclerosis (SSc). SSc is a multisystem autoimmune and vascular disease resulting in extensive fibrosis of the skin and various internal organs. Since SSc pathogenesis is believed to include all the critical components regulating tissue fibrosis, the studies on anti-fibrotic drugs against SSc provide us much useful information regarding the strategy for the treatment of various skin fibrotic conditions. In the recent decade, as is the case with other autoimmune and inflammatory diseases, the molecular targeting therapy with monoclonal antibody has been clinically well examined in SSc. Promising clinical outcomes are so far reported in tocilizumab (an anti-IL-6 receptor antibody), rituximab (an anti-CD20 antibody), and fresolimumab (an anti-TGF-β antibody). The analysis of gene expression profiles in skin lesions of SSc patients treated with tocilizumab or fresolimumab revealed a critical role of monocyte-macrophage lineage cells in the development of skin fibrosis and the involvement of IL-6 and TGF-β in the activation of those cells. Considering that B cells modulate the differentiation and activation of macrophages, favorable clinical outcomes of rituximab treatment imply the central role of B cell/monocyte-macrophage lineage cell axis in the pathogenesis of SSc. This scenario may be applicable at least partly to other skin fibrotic conditions. In this review article, the currently available data on these drugs are summarized and the future directions are discussed.
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Affiliation(s)
- Yoshihide Asano
- Department of Dermatology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655 Japan
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Schönbach C, Verma C, Bond PJ, Ranganathan S. Bioinformatics and systems biology research update from the 15 th International Conference on Bioinformatics (InCoB2016). BMC Bioinformatics 2016; 17:524. [PMID: 28155668 PMCID: PMC5259976 DOI: 10.1186/s12859-016-1409-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The International Conference on Bioinformatics (InCoB) has been publishing peer-reviewed conference papers in BMC Bioinformatics since 2006. Of the 44 articles accepted for publication in supplement issues of BMC Bioinformatics, BMC Genomics, BMC Medical Genomics and BMC Systems Biology, 24 articles with a bioinformatics or systems biology focus are reviewed in this editorial. InCoB2017 is scheduled to be held in Shenzen, China, September 20-22, 2017.
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Affiliation(s)
- Christian Schönbach
- International Research Center for Medical Sciences, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, 860-0811 Japan
| | - Chandra Verma
- Bioinformatics Institute, Agency for Science, Technology and Research (A∗STAR), Singapore, 138671 Singapore
| | - Peter J. Bond
- Bioinformatics Institute, Agency for Science, Technology and Research (A∗STAR), Singapore, 138671 Singapore
| | - Shoba Ranganathan
- Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109 Australia
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