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Han N, Liu Z. Targeting alternative splicing in cancer immunotherapy. Front Cell Dev Biol 2023; 11:1232146. [PMID: 37635865 PMCID: PMC10450511 DOI: 10.3389/fcell.2023.1232146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/01/2023] [Indexed: 08/29/2023] Open
Abstract
Tumor immunotherapy has made great progress in cancer treatment but still faces several challenges, such as a limited number of targetable antigens and varying responses among patients. Alternative splicing (AS) is an essential process for the maturation of nearly all mammalian mRNAs. Recent studies show that AS contributes to expanding cancer-specific antigens and modulating immunogenicity, making it a promising solution to the above challenges. The organoid technology preserves the individual immune microenvironment and reduces the time/economic costs of the experiment model, facilitating the development of splicing-based immunotherapy. Here, we summarize three critical roles of AS in immunotherapy: resources for generating neoantigens, targets for immune-therapeutic modulation, and biomarkers to guide immunotherapy options. Subsequently, we highlight the benefits of adopting organoids to develop AS-based immunotherapies. Finally, we discuss the current challenges in studying AS-based immunotherapy in terms of existing bioinformatics algorithms and biological technologies.
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Affiliation(s)
- Nan Han
- Chinese Academy of Sciences Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhaoqi Liu
- Chinese Academy of Sciences Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
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2
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Weatherley G, Araujo RP, Dando SJ, Jenner AL. Could Mathematics be the Key to Unlocking the Mysteries of Multiple Sclerosis? Bull Math Biol 2023; 85:75. [PMID: 37382681 PMCID: PMC10310626 DOI: 10.1007/s11538-023-01181-0] [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] [Received: 03/26/2023] [Accepted: 06/19/2023] [Indexed: 06/30/2023]
Abstract
Multiple sclerosis (MS) is an autoimmune, neurodegenerative disease that is driven by immune system-mediated demyelination of nerve axons. While diseases such as cancer, HIV, malaria and even COVID have realised notable benefits from the attention of the mathematical community, MS has received significantly less attention despite the increasing disease incidence rates, lack of curative treatment, and long-term impact on patient well-being. In this review, we highlight existing, MS-specific mathematical research and discuss the outstanding challenges and open problems that remain for mathematicians. We focus on how both non-spatial and spatial deterministic models have been used to successfully further our understanding of T cell responses and treatment in MS. We also review how agent-based models and other stochastic modelling techniques have begun to shed light on the highly stochastic and oscillatory nature of this disease. Reviewing the current mathematical work in MS, alongside the biology specific to MS immunology, it is clear that mathematical research dedicated to understanding immunotherapies in cancer or the immune responses to viral infections could be readily translatable to MS and might hold the key to unlocking some of its mysteries.
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Affiliation(s)
- Georgia Weatherley
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Robyn P Araujo
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Samantha J Dando
- School of Biomedical Sciences, Centre for Immunology and Infection Control, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Adrianne L Jenner
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.
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3
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Reaction-diffusion models in weighted and directed connectomes. PLoS Comput Biol 2022; 18:e1010507. [DOI: 10.1371/journal.pcbi.1010507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 11/23/2022] [Accepted: 08/22/2022] [Indexed: 11/07/2022] Open
Abstract
Connectomes represent comprehensive descriptions of neural connections in a nervous system to better understand and model central brain function and peripheral processing of afferent and efferent neural signals. Connectomes can be considered as a distinctive and necessary structural component alongside glial, vascular, neurochemical, and metabolic networks of the nervous systems of higher organisms that are required for the control of body functions and interaction with the environment. They are carriers of functional epiphenomena such as planning behavior and cognition, which are based on the processing of highly dynamic neural signaling patterns. In this study, we examine more detailed connectomes with edge weighting and orientation properties, in which reciprocal neuronal connections are also considered. Diffusion processes are a further necessary condition for generating dynamic bioelectric patterns in connectomes. Based on our high-precision connectome data, we investigate different diffusion-reaction models to study the propagation of dynamic concentration patterns in control and lesioned connectomes. Therefore, differential equations for modeling diffusion were combined with well-known reaction terms to allow the use of connection weights, connectivity orientation and spatial distances.
Three reaction-diffusion systems Gray-Scott, Gierer-Meinhardt and Mimura-Murray were investigated. For this purpose, implicit solvers were implemented in a numerically stable reaction-diffusion system within the framework of neuroVIISAS. The implemented reaction-diffusion systems were applied to a subconnectome which shapes the mechanosensitive pathway that is strongly affected in the multiple sclerosis demyelination disease. It was found that demyelination modeling by connectivity weight modulation changes the oscillations of the target region, i.e. the primary somatosensory cortex, of the mechanosensitive pathway.
In conclusion, a new application of reaction-diffusion systems to weighted and directed connectomes has been realized. Because the implementation were performed in the neuroVIISAS framework many possibilities for the study of dynamic reaction-diffusion processes in empirical connectomes as well as specific randomized network models are available now.
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4
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Lafuente-Gracia L, Borgiani E, Nasello G, Geris L. Towards in silico Models of the Inflammatory Response in Bone Fracture Healing. Front Bioeng Biotechnol 2021; 9:703725. [PMID: 34660547 PMCID: PMC8514728 DOI: 10.3389/fbioe.2021.703725] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 09/07/2021] [Indexed: 12/21/2022] Open
Abstract
In silico modeling is a powerful strategy to investigate the biological events occurring at tissue, cellular and subcellular level during bone fracture healing. However, most current models do not consider the impact of the inflammatory response on the later stages of bone repair. Indeed, as initiator of the healing process, this early phase can alter the regenerative outcome: if the inflammatory response is too strongly down- or upregulated, the fracture can result in a non-union. This review covers the fundamental information on fracture healing, in silico modeling and experimental validation. It starts with a description of the biology of fracture healing, paying particular attention to the inflammatory phase and its cellular and subcellular components. We then discuss the current state-of-the-art regarding in silico models of the immune response in different tissues as well as the bone regeneration process at the later stages of fracture healing. Combining the aforementioned biological and computational state-of-the-art, continuous, discrete and hybrid modeling technologies are discussed in light of their suitability to capture adequately the multiscale course of the inflammatory phase and its overall role in the healing outcome. Both in the establishment of models as in their validation step, experimental data is required. Hence, this review provides an overview of the different in vitro and in vivo set-ups that can be used to quantify cell- and tissue-scale properties and provide necessary input for model credibility assessment. In conclusion, this review aims to provide hands-on guidance for scientists interested in building in silico models as an additional tool to investigate the critical role of the inflammatory phase in bone regeneration.
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Affiliation(s)
- Laura Lafuente-Gracia
- Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium.,Prometheus: Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium
| | - Edoardo Borgiani
- Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium.,Prometheus: Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium.,Biomechanics Research Unit, GIGA in silico Medicine, University of Liège, Liège, Belgium
| | - Gabriele Nasello
- Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium.,Prometheus: Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium.,Skeletal Biology and Engineering Research Center, KU Leuven, Leuven, Belgium
| | - Liesbet Geris
- Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium.,Prometheus: Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium.,Biomechanics Research Unit, GIGA in silico Medicine, University of Liège, Liège, Belgium.,Skeletal Biology and Engineering Research Center, KU Leuven, Leuven, Belgium
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5
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Pernice S, Follia L, Maglione A, Pennisi M, Pappalardo F, Novelli F, Clerico M, Beccuti M, Cordero F, Rolla S. Computational modeling of the immune response in multiple sclerosis using epimod framework. BMC Bioinformatics 2020; 21:550. [PMID: 33308135 PMCID: PMC7734848 DOI: 10.1186/s12859-020-03823-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 10/19/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Multiple Sclerosis (MS) represents nowadays in Europe the leading cause of non-traumatic disabilities in young adults, with more than 700,000 EU cases. Although huge strides have been made over the years, MS etiology remains partially unknown. Furthermore, the presence of various endogenous and exogenous factors can greatly influence the immune response of different individuals, making it difficult to study and understand the disease. This becomes more evident in a personalized-fashion when medical doctors have to choose the best therapy for patient well-being. In this optics, the use of stochastic models, capable of taking into consideration all the fluctuations due to unknown factors and individual variability, is highly advisable. RESULTS We propose a new model to study the immune response in relapsing remitting MS (RRMS), the most common form of MS that is characterized by alternate episodes of symptom exacerbation (relapses) with periods of disease stability (remission). In this new model, both the peripheral lymph node/blood vessel and the central nervous system are explicitly represented. The model was created and analysed using Epimod, our recently developed general framework for modeling complex biological systems. Then the effectiveness of our model was shown by modeling the complex immunological mechanisms characterizing RRMS during its course and under the DAC administration. CONCLUSIONS Simulation results have proven the ability of the model to reproduce in silico the immune T cell balance characterizing RRMS course and the DAC effects. Furthermore, they confirmed the importance of a timely intervention on the disease course.
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Affiliation(s)
- Simone Pernice
- Department of Computer Science, University of Turin, Turin, Italy
| | - Laura Follia
- Department of Computer Science, University of Turin, Turin, Italy.,Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
| | - Alessandro Maglione
- Department of Clinical and Biological Sciences, University of Turin, Orbassano, Italy
| | - Marzio Pennisi
- Computer Science Inst., DiSIT, University of Eastern Piedmont, Alessandria, Italy
| | | | - Francesco Novelli
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
| | - Marinella Clerico
- Department of Clinical and Biological Sciences, University of Turin, Orbassano, Italy
| | - Marco Beccuti
- Department of Computer Science, University of Turin, Turin, Italy.
| | | | - Simona Rolla
- Department of Clinical and Biological Sciences, University of Turin, Orbassano, Italy
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6
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Russo G, Sgroi G, Parasiliti Palumbo GA, Pennisi M, Juarez MA, Cardona PJ, Motta S, Walker KB, Fichera E, Viceconti M, Pappalardo F. Moving forward through the in silico modeling of tuberculosis: a further step with UISS-TB. BMC Bioinformatics 2020; 21:458. [PMID: 33308139 PMCID: PMC7733696 DOI: 10.1186/s12859-020-03762-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 09/17/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND In 2018, about 10 million people were found infected by tuberculosis, with approximately 1.2 million deaths worldwide. Despite these numbers have been relatively stable in recent years, tuberculosis is still considered one of the top 10 deadliest diseases worldwide. Over the years, Mycobacterium tuberculosis has developed a form of resistance to first-line tuberculosis treatments, specifically to isoniazid, leading to multi-drug-resistant tuberculosis. In this context, the EU and Indian DBT funded project STriTuVaD-In Silico Trial for Tuberculosis Vaccine Development-is supporting the identification of new interventional strategies against tuberculosis thanks to the use of Universal Immune System Simulator (UISS), a computational framework capable of predicting the immunity induced by specific drugs such as therapeutic vaccines and antibiotics. RESULTS Here, we present how UISS accurately simulates tuberculosis dynamics and its interaction within the immune system, and how it predicts the efficacy of the combined action of isoniazid and RUTI vaccine in a specific digital population cohort. Specifically, we simulated two groups of 100 digital patients. The first group was treated with isoniazid only, while the second one was treated with the combination of RUTI vaccine and isoniazid, according to the dosage strategy described in the clinical trial design. UISS-TB shows to be in good agreement with clinical trial results suggesting that RUTI vaccine may favor a partial recover of infected lung tissue. CONCLUSIONS In silico trials innovations represent a powerful pipeline for the prediction of the effects of specific therapeutic strategies and related clinical outcomes. Here, we present a further step in UISS framework implementation. Specifically, we found that the simulated mechanism of action of RUTI and INH are in good alignment with the results coming from past clinical phase IIa trials.
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Affiliation(s)
- Giulia Russo
- Department of Drug Sciences, University of Catania, 95125 Catania, Italy
| | - Giuseppe Sgroi
- Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy
| | | | - Marzio Pennisi
- Computer Science Institute, DiSIT, University of Eastern Piedmont, 15121 Alessandria, Italy
| | - Miguel A. Juarez
- School of Mathematics and Statistics, University of Sheffield, Sheffield, S3 7RH UK
| | - Pere-Joan Cardona
- Archivel Farma, S.L., 08916 Badalona, Spain
- Experimental Tuberculosis Unit (UTE), Fundació Institut Germans Trias I Pujol (IGTP), Universitat Autònoma de Barcelona (UAB), Badalona, Spain
- Centro de Investigación Biomédica en Red (CIBER) de Enfermedades Respiratorias, Madrid, Spain
| | - Santo Motta
- National Research Council of Italy, 00185 Rome, Italy
| | - Kenneth B. Walker
- TuBerculosis Vaccine Initiative (TBVI), Lelystad, 8219 The Netherlands
| | | | - Marco Viceconti
- Department of Industrial Engineering, University of Bologna, 40136 Bologna, Italy
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Qiu R, Zhou L, Ma Y, Zhou L, Liang T, Shi L, Long J, Yuan D. Regulatory T Cell Plasticity and Stability and Autoimmune Diseases. Clin Rev Allergy Immunol 2020; 58:52-70. [PMID: 30449014 DOI: 10.1007/s12016-018-8721-0] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
CD4+CD25+ regulatory T cells (Tregs) are a class of CD4+ T cells with immunosuppressive functions that play a critical role in maintaining immune homeostasis. However, in certain disease settings, Tregs demonstrate plastic differentiation, and the stability of these Tregs, which is characterized by the stable expression or protective epigenetic modifications of the transcription factor Foxp3, becomes abnormal. Plastic Tregs have some features of helper T (Th) cells, such as the secretion of Th-related cytokines and the expression of specific transcription factors in Th cells, but also still retain the expression of Foxp3, a feature of Tregs. Although such Th-like Tregs can secrete pro-inflammatory cytokines, they still possess a strong ability to inhibit specific Th cell responses. Therefore, the plastic differentiation of Tregs not only increases the complexity of the immune circumstances under pathological conditions, especially autoimmune diseases, but also shows an association with changes in the stability of Tregs. The plastic differentiation and stability change of Tregs play vital roles in the progression of diseases. This review focuses on the phenotypic characteristics, functions, and formation conditions of several plastic Tregs and also summarizes the changes of Treg stability and their effects on inhibitory function. Additionally, the effects of Treg plasticity and stability on disease prognosis for several autoimmune diseases were also investigated in order to better understand the relationship between Tregs and autoimmune diseases.
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Affiliation(s)
- Runze Qiu
- School of Pharmacy, Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica, Nanjing University of Chinese Medicine, Xianlin Dadao 138, Nanjing, 210023, People's Republic of China
| | - Liyu Zhou
- School of Pharmacy, Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica, Nanjing University of Chinese Medicine, Xianlin Dadao 138, Nanjing, 210023, People's Republic of China
| | - Yuanjing Ma
- School of Pharmacy, Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica, Nanjing University of Chinese Medicine, Xianlin Dadao 138, Nanjing, 210023, People's Republic of China
| | - Lingling Zhou
- School of Pharmacy, Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica, Nanjing University of Chinese Medicine, Xianlin Dadao 138, Nanjing, 210023, People's Republic of China
| | - Tao Liang
- School of Pharmacy, Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica, Nanjing University of Chinese Medicine, Xianlin Dadao 138, Nanjing, 210023, People's Republic of China
| | - Le Shi
- School of Pharmacy, Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica, Nanjing University of Chinese Medicine, Xianlin Dadao 138, Nanjing, 210023, People's Republic of China
| | - Jun Long
- School of Pharmacy, Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica, Nanjing University of Chinese Medicine, Xianlin Dadao 138, Nanjing, 210023, People's Republic of China.
| | - Dongping Yuan
- School of Pharmacy, Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica, Nanjing University of Chinese Medicine, Xianlin Dadao 138, Nanjing, 210023, People's Republic of China.
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8
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Siokis A, Robert PA, Meyer-Hermann M. Agent-Based Modeling of T Cell Receptor Cooperativity. Int J Mol Sci 2020; 21:ijms21186473. [PMID: 32899840 PMCID: PMC7555007 DOI: 10.3390/ijms21186473] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 09/01/2020] [Accepted: 09/02/2020] [Indexed: 11/25/2022] Open
Abstract
Immunological synapse (IS) formation is a key event during antigen recognition by T cells. Recent experimental evidence suggests that the affinity between T cell receptors (TCRs) and antigen is actively modulated during the early steps of TCR signaling. In this work, we used an agent-based model to study possible mechanisms for affinity modulation during IS formation. We show that, without any specific active mechanism, the observed affinity between receptors and ligands evolves over time and depends on the density of ligands of the antigen peptide presented by major histocompatibility complexes (pMHC) and TCR molecules. A comparison between the presence or absence of TCR–pMHC centrally directed flow due to F-actin coupling suggests that centripetal transport is a potential mechanism for affinity modulation. The model further suggests that the time point of affinity measurement during immune synapse formation is critical. Finally, a mathematical model of F-actin foci formation incorporated in the agent-based model shows that TCR affinity can potentially be actively modulated by positive/negative feedback of the F-actin foci on the TCR-pMHC association rate kon.
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Affiliation(s)
- Anastasios Siokis
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, 38106 Braunschweig, Germany; (A.S.); (P.A.R.)
| | - Philippe A. Robert
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, 38106 Braunschweig, Germany; (A.S.); (P.A.R.)
| | - Michael Meyer-Hermann
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, 38106 Braunschweig, Germany; (A.S.); (P.A.R.)
- Institute of Biochemistry, Biotechnology and Bioinformatics, Technische Universität Braunschweig, 38106 Braunschweig, Germany
- Correspondence: ; Tel.: +49-531-391-55210
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Russo G, Reche P, Pennisi M, Pappalardo F. The combination of artificial intelligence and systems biology for intelligent vaccine design. Expert Opin Drug Discov 2020; 15:1267-1281. [PMID: 32662677 DOI: 10.1080/17460441.2020.1791076] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
INTRODUCTION A new body of evidence depicts the applications of artificial intelligence and systems biology in vaccine design and development. The combination of both approaches shall revolutionize healthcare, accelerating clinical trial processes and reducing the costs and time involved in drug research and development. AREAS COVERED This review explores the basics of artificial intelligence and systems biology approaches in the vaccine development pipeline. The topics include a detailed description of epitope prediction tools for designing epitope-based vaccines and agent-based models for immune system response prediction, along with a focus on their potentiality to facilitate clinical trial phases. EXPERT OPINION Artificial intelligence and systems biology offer the opportunity to avoid the inefficiencies and failures that arise in the classical vaccine development pipeline. One promising solution is the combination of both methodologies in a multiscale perspective through an accurate pipeline. We are entering an 'in silico era' in which scientific partnerships, including a more and more increasing creation of an 'ecosystem' of collaboration and multidisciplinary approach, are relevant for addressing the long and risky road of vaccine discovery and development. In this context, regulatory guidance should be developed to qualify the in silico trials as evidence for intelligent vaccine development.
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Affiliation(s)
- Giulia Russo
- Department of Drug Sciences, University of Catania , Catania, Italy
| | - Pedro Reche
- Department of Immunology, Universidad Complutense De Madrid, Ciudad Universitaria , Madrid, Spain
| | - Marzio Pennisi
- Computer Science Institute, DiSIT, University of Eastern Piedmont , Italy
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10
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Pappalardo F, Russo G, Pennisi M, Parasiliti Palumbo GA, Sgroi G, Motta S, Maimone D. The Potential of Computational Modeling to Predict Disease Course and Treatment Response in Patients with Relapsing Multiple Sclerosis. Cells 2020; 9:E586. [PMID: 32121606 PMCID: PMC7140535 DOI: 10.3390/cells9030586] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 02/26/2020] [Accepted: 02/27/2020] [Indexed: 01/10/2023] Open
Abstract
As of today, 20 disease-modifying drugs (DMDs) have been approved for the treatment of relapsing multiple sclerosis (MS) and, based on their efficacy, they can be grouped into moderate-efficacy DMDs and high-efficacy DMDs. The choice of the drug mostly relies on the judgment and experience of neurologists and the evaluation of the therapeutic response can only be obtained by monitoring the clinical and magnetic resonance imaging (MRI) status during follow up. In an era where therapies are focused on personalization, this study aims to develop a modeling infrastructure to predict the evolution of relapsing MS and the response to treatments. We built a computational modeling infrastructure named Universal Immune System Simulator (UISS), which can simulate the main features and dynamics of the immune system activities. We extended UISS to simulate all the underlying MS pathogenesis and its interaction with the host immune system. This simulator is a multi-scale, multi-organ, agent-based simulator with an attached module capable of simulating the dynamics of specific biological pathways at the molecular level. We simulated six MS patients with different relapsing-remitting courses. These patients were characterized based on their age, sex, presence of oligoclonal bands, therapy, and MRI lesion load at the onset. The simulator framework is made freely available and can be used following the links provided in the availability section. Even though the model can be further personalized employing immunological parameters and genetic information, we generated a few simulation scenarios for each patient based on the available data. Among these simulations, it was possible to find the scenarios that realistically matched the real clinical and MRI history. Moreover, for two patients, the simulator anticipated the timing of subsequent relapses, which occurred, suggesting that UISS may have the potential to assist MS specialists in predicting the course of the disease and the response to treatment.
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Affiliation(s)
| | - Giulia Russo
- Department of Drug Sciences, University of Catania, 95125 Catania, Italy;
| | - Marzio Pennisi
- Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy; (M.P.); (G.A.P.P.); (G.S.)
| | | | - Giuseppe Sgroi
- Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy; (M.P.); (G.A.P.P.); (G.S.)
| | - Santo Motta
- National Research Council of Italy, 00185 Rome, Italy;
| | - Davide Maimone
- Multiple Sclerosis Center, Neurology Unit, Garibaldi Hospital, 95124 Catania, Italy;
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11
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Pennisi M, Russo G, Sgroi G, Bonaccorso A, Parasiliti Palumbo GA, Fichera E, Mitra DK, Walker KB, Cardona PJ, Amat M, Viceconti M, Pappalardo F. Predicting the artificial immunity induced by RUTI® vaccine against tuberculosis using universal immune system simulator (UISS). BMC Bioinformatics 2019; 20:504. [PMID: 31822272 PMCID: PMC6904993 DOI: 10.1186/s12859-019-3045-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 08/21/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Tuberculosis (TB) represents a worldwide cause of mortality (it infects one third of the world's population) affecting mostly developing countries, including India, and recently also developed ones due to the increased mobility of the world population and the evolution of different new bacterial strains capable to provoke multi-drug resistance phenomena. Currently, antitubercular drugs are unable to eradicate subpopulations of Mycobacterium tuberculosis (MTB) bacilli and therapeutic vaccinations have been postulated to overcome some of the critical issues related to the increase of drug-resistant forms and the difficult clinical and public health management of tuberculosis patients. The Horizon 2020 EC funded project "In Silico Trial for Tuberculosis Vaccine Development" (STriTuVaD) to support the identification of new therapeutic interventions against tuberculosis through novel in silico modelling of human immune responses to disease and vaccines, thereby drastically reduce the cost of clinical trials in this critical sector of public healthcare. RESULTS We present the application of the Universal Immune System Simulator (UISS) computational modeling infrastructure as a disease model for TB. The model is capable to simulate the main features and dynamics of the immune system activities i.e., the artificial immunity induced by RUTI® vaccine, a polyantigenic liposomal therapeutic vaccine made of fragments of Mycobacterium tuberculosis cells (FCMtb). Based on the available data coming from phase II Clinical Trial in subjects with latent tuberculosis infection treated with RUTI® and isoniazid, we generated simulation scenarios through validated data in order to tune UISS accordingly to STriTuVaD objectives. The first case simulates the establishment of MTB latent chronic infection with some typical granuloma formation; the second scenario deals with a reactivation phase during latent chronic infection; the third represents the latent chronic disease infection scenario during RUTI® vaccine administration. CONCLUSIONS The application of this computational modeling strategy helpfully contributes to simulate those mechanisms involved in the early stages and in the progression of tuberculosis infection and to predict how specific therapeutical strategies will act in this scenario. In view of these results, UISS owns the capacity to open the door for a prompt integration of in silico methods within the pipeline of clinical trials, supporting and guiding the testing of treatments in patients affected by tuberculosis.
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Affiliation(s)
- Marzio Pennisi
- Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy
| | - Giulia Russo
- Department of Drug Sciences, University of Catania, Italy, 95125 Catania, Italy
| | - Giuseppe Sgroi
- Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy
| | - Angela Bonaccorso
- Department of Drug Sciences, University of Catania, Italy, 95125 Catania, Italy
| | | | | | - Dipendra Kumar Mitra
- Department of Transplant Immunology and Immunogenetics, All India Institute of Medical Sciences, New Delhi, 110029 India
| | - Kenneth B. Walker
- TuBerculosis Vaccine Initiative (TBVI), Lelystad, 8219 The Netherlands
| | - Pere-Joan Cardona
- Archivel Farma, S.L, 08916 Badalona, Spain
- Experimental Tuberculosis Unit (UTE), Fundació Institut Germans Trias i Pujol (IGTP), Universitat Autònoma de Barcelona (UAB), Badalona, Spain
- Centro de Investigación Biomédica en Red (CIBER) de Enfermedades Respiratorias, Madrid, Spain
| | - Merce Amat
- Archivel Farma, S.L, 08916 Badalona, Spain
| | - Marco Viceconti
- Department of Industrial Engineering, Alma Mater Studiorum, University of Bologna, 40136 Bologna, Italy
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12
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Pernice S, Pennisi M, Romano G, Maglione A, Cutrupi S, Pappalardo F, Balbo G, Beccuti M, Cordero F, Calogero RA. A computational approach based on the colored Petri net formalism for studying multiple sclerosis. BMC Bioinformatics 2019; 20:623. [PMID: 31822261 PMCID: PMC6904991 DOI: 10.1186/s12859-019-3196-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 11/05/2019] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Multiple Sclerosis (MS) is an immune-mediated inflammatory disease of the Central Nervous System (CNS) which damages the myelin sheath enveloping nerve cells thus causing severe physical disability in patients. Relapsing Remitting Multiple Sclerosis (RRMS) is one of the most common form of MS in adults and is characterized by a series of neurologic symptoms, followed by periods of remission. Recently, many treatments were proposed and studied to contrast the RRMS progression. Among these drugs, daclizumab (commercial name Zinbryta), an antibody tailored against the Interleukin-2 receptor of T cells, exhibited promising results, but its efficacy was accompanied by an increased frequency of serious adverse events. Manifested side effects consisted of infections, encephalitis, and liver damages. Therefore daclizumab has been withdrawn from the market worldwide. Another interesting case of RRMS regards its progression in pregnant women where a smaller incidence of relapses until the delivery has been observed. RESULTS In this paper we propose a new methodology for studying RRMS, which we implemented in GreatSPN, a state-of-the-art open-source suite for modelling and analyzing complex systems through the Petri Net (PN) formalism. This methodology exploits: (a) an extended Colored PN formalism to provide a compact graphical description of the system and to automatically derive a set of ODEs encoding the system dynamics and (b) the Latin Hypercube Sampling with PRCC index to calibrate ODE parameters for reproducing the real behaviours in healthy and MS subjects.To show the effectiveness of such methodology a model of RRMS has been constructed and studied. Two different scenarios of RRMS were thus considered. In the former scenario the effect of the daclizumab administration is investigated, while in the latter one RRMS was studied in pregnant women. CONCLUSIONS We propose a new computational methodology to study RRMS disease. Moreover, we show that model generated and calibrated according to this methodology is able to reproduce the expected behaviours.
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Affiliation(s)
- Simone Pernice
- Department of Computer Science, University of Turin, Turin, Italy
| | - Marzio Pennisi
- Department of Mathematics and Computer Science, University of Catania, Catania, Italy
| | - Greta Romano
- Department of Computer Science, University of Turin, Turin, Italy
| | - Alessandro Maglione
- Department of Clinical and Biological Sciences, University of Turin, Orbassano, Italy
| | - Santina Cutrupi
- Department of Clinical and Biological Sciences, University of Turin, Orbassano, Italy
| | | | - Gianfranco Balbo
- Department of Computer Science, University of Turin, Turin, Italy
| | - Marco Beccuti
- Department of Computer Science, University of Turin, Turin, Italy.
| | | | - Raffaele A Calogero
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
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13
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Pappalardo F, Rajput AM, Motta S. Computational modeling of brain pathologies: the case of multiple sclerosis. Brief Bioinform 2019; 19:318-324. [PMID: 28011755 DOI: 10.1093/bib/bbw123] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Indexed: 01/09/2023] Open
Abstract
The central nervous system is the most complex network of the human body. The existence and functionality of a large number of molecular species in human brain are still ambiguous and mostly unknown, thus posing a challenge to Science and Medicine. Neurological diseases inherit the same level of complexity, making effective treatments difficult to be found. Multiple sclerosis (MS) is a major neurological disease that causes severe inabilities and also a significant social burden on health care system: between 2 and 2.5 million people are affected by it, and the cost associated with it is significantly higher as compared with other neurological diseases because of the chronic nature of the disease and to the partial efficacy of current therapies. Despite difficulties in understanding and treating MS, many computational models have been developed to help neurologists. In the present work, we briefly review the main characteristics of MS and present a selection criteria of modeling approaches.
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Affiliation(s)
| | | | - Santo Motta
- Istitute for Applied Calculus (IAC) "M. Picone", National Research Council of Italy (CNR), Italy
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14
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Kannan V, Kiani NA, Piehl F, Tegner J. A minimal unified model of disease trajectories captures hallmarks of multiple sclerosis. Math Biosci 2017; 289:1-8. [PMID: 28365299 DOI: 10.1016/j.mbs.2017.03.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Revised: 03/04/2017] [Accepted: 03/16/2017] [Indexed: 01/04/2023]
Abstract
Multiple Sclerosis (MS) is an autoimmune disease targeting the central nervous system (CNS) causing demyelination and neurodegeneration leading to accumulation of neurological disability. Here we present a minimal, computational model involving the immune system and CNS that generates the principal subtypes of the disease observed in patients. The model captures several key features of MS, especially those that distinguish the chronic progressive phase from that of the relapse-remitting. In addition, a rare subtype of the disease, progressive relapsing MS naturally emerges from the model. The model posits the existence of two key thresholds, one in the immune system and the other in the CNS, that separate dynamically distinct behavior of the model. Exploring the two-dimensional space of these thresholds, we obtain multiple phases of disease evolution and these shows greater variation than the clinical classification of MS, thus capturing the heterogeneity that is manifested in patients.
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Affiliation(s)
- Venkateshan Kannan
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Solna, Karolinska Institutet 17176, Sweden
| | - Narsis A Kiani
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Solna, Karolinska Institutet 17176, Sweden
| | - Fredrik Piehl
- Unit of NeuroImmunology, Center for Molecular Medicine, Department of Clinical Neuroscience, Karolinska University Hospital L8 17176, Stockholm, Sweden
| | - Jesper Tegner
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Solna, Karolinska Institutet 17176, Sweden; Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
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15
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Lombardo MC, Barresi R, Bilotta E, Gargano F, Pantano P, Sammartino M. Demyelination patterns in a mathematical model of multiple sclerosis. J Math Biol 2016; 75:373-417. [DOI: 10.1007/s00285-016-1087-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2016] [Revised: 11/25/2016] [Indexed: 11/29/2022]
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16
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Pennisi M, Cavalieri S, Motta S, Pappalardo F. A methodological approach for using high-level Petri Nets to model the immune system response. BMC Bioinformatics 2016; 17:498. [PMID: 28155706 PMCID: PMC5259858 DOI: 10.1186/s12859-016-1361-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Mathematical and computational models showed to be a very important support tool for the comprehension of the immune system response against pathogens. Models and simulations allowed to study the immune system behavior, to test biological hypotheses about diseases and infection dynamics, and to improve and optimize novel and existing drugs and vaccines. Continuous models, mainly based on differential equations, usually allow to qualitatively study the system but lack in description; conversely discrete models, such as agent based models and cellular automata, permit to describe in detail entities properties at the cost of losing most qualitative analyses. Petri Nets (PN) are a graphical modeling tool developed to model concurrency and synchronization in distributed systems. Their use has become increasingly marked also thanks to the introduction in the years of many features and extensions which lead to the born of "high level" PN. RESULTS We propose a novel methodological approach that is based on high level PN, and in particular on Colored Petri Nets (CPN), that can be used to model the immune system response at the cellular scale. To demonstrate the potentiality of the approach we provide a simple model of the humoral immune system response that is able of reproducing some of the most complex well-known features of the adaptive response like memory and specificity features. CONCLUSIONS The methodology we present has advantages of both the two classical approaches based on continuous and discrete models, since it allows to gain good level of granularity in the description of cells behavior without losing the possibility of having a qualitative analysis. Furthermore, the presented methodology based on CPN allows the adoption of the same graphical modeling technique well known to life scientists that use PN for the modeling of signaling pathways. Finally, such an approach may open the floodgates to the realization of multi scale models that integrate both signaling pathways (intra cellular) models and cellular (population) models built upon the same technique and software.
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Affiliation(s)
- Marzio Pennisi
- Department of Mathematics and Computer Science, University of Catania, Catania, Italy
| | - Salvatore Cavalieri
- Department of Electrical Electronic and Computer Engineering (DIEEI), University of Catania, Catania, Italy
| | - Santo Motta
- Department of Mathematics and Computer Science, University of Catania, Catania, Italy
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Pappalardo F, Pennisi M. Agent based simulations in disease modeling Comment on "Towards a unified approach in the modeling of fibrosis: A review with research perspectives" by Martine Ben Amar and Carlo Bianca. Phys Life Rev 2016; 17:110-1. [PMID: 27173053 DOI: 10.1016/j.plrev.2016.05.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2016] [Accepted: 05/04/2016] [Indexed: 10/21/2022]
Affiliation(s)
- Francesco Pappalardo
- Dipartimento di Scienze del Farmaco, Università di Catania, Viale Andrea Doria 6, 95125 Catania, Italy.
| | - Marzio Pennisi
- Dipartimento di Matematica e Informatica, Università di Catania, Viale Andrea Doria 6, 95125 Catania, Italy.
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Pennisi M, Russo G, Motta S, Pappalardo F. Agent based modeling of the effects of potential treatments over the blood-brain barrier in multiple sclerosis. J Immunol Methods 2015; 427:6-12. [PMID: 26343337 DOI: 10.1016/j.jim.2015.08.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2015] [Revised: 06/15/2015] [Accepted: 08/28/2015] [Indexed: 10/23/2022]
Abstract
Multiple sclerosis is a disease of the central nervous system that involves the destruction of the insulating sheath of axons, causing severe disabilities. Since the etiology of the disease is not yet fully understood, the use of novel techniques that may help to understand the disease, to suggest potential therapies and to test the effects of candidate treatments is highly advisable. To this end we developed an agent based model that demonstrated its ability to reproduce the typical oscillatory behavior observed in the most common form of multiple sclerosis, relapsing-remitting multiple sclerosis. The model has then been used to test the potential beneficial effects of vitamin D over the disease. Many scientific studies underlined the importance of the blood-brain barrier and of the mechanisms that influence its permeability on the development of the disease. In the present paper we further extend our previously developed model with a mechanism that mimics the blood-brain barrier behavior. The goal of our work is to suggest the best strategies to follow for developing new potential treatments that intervene in the blood-brain barrier. Results suggest that the best treatments should potentially prevent the opening of the blood-brain barrier, as treatments that help in recovering the blood-brain barrier functionality could be less effective.
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Affiliation(s)
- Marzio Pennisi
- Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy
| | - Giulia Russo
- Department of Drug Science, University of Catania, 95125 Catania, Italy
| | - Santo Motta
- Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy
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Follicular Helper CD4+ T Cells in Human Neuroautoimmune Diseases and Their Animal Models. Mediators Inflamm 2015; 2015:638968. [PMID: 26300592 PMCID: PMC4537760 DOI: 10.1155/2015/638968] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2014] [Accepted: 02/16/2015] [Indexed: 01/27/2023] Open
Abstract
Follicular helper CD4+ T (TFH) cells play a fundamental role in humoral immunity deriving from their ability to provide help for germinal center (GC) formation, B cell differentiation into plasma cells and memory cells, and antibody production in secondary lymphoid tissues. TFH cells can be identified by a combination of markers, including the chemokine receptor CXCR5, costimulatory molecules ICOS and PD-1, transcription repressor Bcl-6, and cytokine IL-21. It is difficult and impossible to get access to secondary lymphoid tissues in humans, so studies are usually performed with human peripheral blood samples as circulating counterparts of tissue TFH cells. A balance of TFH cell generation and function is critical for protective antibody response, whereas overactivation of TFH cells or overexpression of TFH-associated molecules may result in autoimmune diseases. Emerging data have shown that TFH cells and TFH-associated molecules may be involved in the pathogenesis of neuroautoimmune diseases including multiple sclerosis (MS), neuromyelitis optica (NMO)/neuromyelitis optica spectrum disorders (NMOSD), and myasthenia gravis (MG). This review summarizes the features of TFH cells, including their development, function, and roles as well as TFH-associated molecules in neuroautoimmune diseases and their animal models.
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Agent-based modeling of the immune system: NetLogo, a promising framework. BIOMED RESEARCH INTERNATIONAL 2014; 2014:907171. [PMID: 24864263 PMCID: PMC4016927 DOI: 10.1155/2014/907171] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/27/2014] [Accepted: 04/02/2014] [Indexed: 12/12/2022]
Abstract
Several components that interact with each other to evolve a complex, and, in some cases, unexpected behavior, represents one of the main and fascinating features of the mammalian immune system. Agent-based modeling and cellular automata belong to a class of discrete mathematical approaches in which entities (agents) sense local information and undertake actions over time according to predefined rules. The strength of this approach is characterized by the appearance of a global behavior that emerges from interactions among agents. This behavior is unpredictable, as it does not follow linear rules. There are a lot of works that investigates the immune system with agent-based modeling and cellular automata. They have shown the ability to see clearly and intuitively into the nature of immunological processes. NetLogo is a multiagent programming language and modeling environment for simulating complex phenomena. It is designed for both research and education and is used across a wide range of disciplines and education levels. In this paper, we summarize NetLogo applications to immunology and, particularly, how this framework can help in the development and formulation of hypotheses that might drive further experimental investigations of disease mechanisms.
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In silico modeling of the immune system: cellular and molecular scale approaches. BIOMED RESEARCH INTERNATIONAL 2014; 2014:371809. [PMID: 24804217 PMCID: PMC3997136 DOI: 10.1155/2014/371809] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/27/2014] [Accepted: 03/05/2014] [Indexed: 11/17/2022]
Abstract
The revolutions in biotechnology and information technology have produced clinical data, which complement biological data. These data enable detailed descriptions of various healthy and diseased states and responses to therapies. For the investigation of the physiology and pathology of the immune responses, computer and mathematical models have been used in the last decades, enabling the representation of biological processes. In this modeling effort, a major issue is represented by the communication between models that work at cellular and molecular level, that is, multiscale representation. Here we sketch some attempts to model immune system dynamics at both levels.
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