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Komarova NL, Rignot C, Fleischman AG, Wodarz D. Dynamically adjusted cell fate decisions and resilience to mutant invasion during steady-state hematopoiesis revealed by an experimentally parameterized mathematical model. Proc Natl Acad Sci U S A 2024; 121:e2321525121. [PMID: 39250660 PMCID: PMC11420203 DOI: 10.1073/pnas.2321525121] [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: 12/07/2023] [Accepted: 07/17/2024] [Indexed: 09/11/2024] Open
Abstract
A major next step in hematopoietic stem cell (HSC) biology is to enhance our quantitative understanding of cellular and evolutionary dynamics involved in undisturbed hematopoiesis. Mathematical models have been and continue to be key in this respect, and are most powerful when parameterized experimentally and containing sufficient biological complexity. In this paper, we use data from label propagation experiments in mice to parameterize a mathematical model of hematopoiesis that includes homeostatic control mechanisms as well as clonal evolution. We find that nonlinear feedback control can drastically change the interpretation of kinetic estimates at homeostasis. This suggests that short-term HSC and multipotent progenitors can dynamically adjust to sustain themselves temporarily in the absence of long-term HSCs, even if they differentiate more often than they self-renew in undisturbed homeostasis. Additionally, the presence of feedback control in the model renders the system resilient against mutant invasion. Invasion barriers, however, can be overcome by a combination of age-related changes in stem cell differentiation and evolutionary niche construction dynamics based on a mutant-associated inflammatory environment. This helps us understand the evolution of e.g., TET2 or DNMT3A mutants, and how to potentially reduce mutant burden.
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Affiliation(s)
- Natalia L. Komarova
- Department of Mathematics, University of California San Diego, La Jolla, CA92093
| | - Chiara Rignot
- Department of Mathematics, University of California Irvine, Irvine, CA92697
| | | | - Dominik Wodarz
- Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, CA92093
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Iyaniwura SA, Cassidy T, Ribeiro RM, Perelson AS. A multiscale model of the action of a capsid assembly modulator for the treatment of chronic hepatitis B. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.16.603658. [PMID: 39071423 PMCID: PMC11275877 DOI: 10.1101/2024.07.16.603658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Chronic hepatitis B virus (HBV) infection is strongly associated with increased risk of liver cancer and cirrhosis. While existing treatments effectively inhibit the HBV life cycle, viral rebound occurs rapidly following treatment interruption. Consequently, functional cure rates of chronic HBV infection remain low and there is increased interest in a novel treatment modality, capsid assembly modulators (CAMs). Here, we develop a multiscale mathematical model of CAM treatment in chronic HBV infection. By fitting the model to participant data from a phase I trial of the first-generation CAM vebicorvir, we estimate the drug's dose-dependent effectiveness and identify the physiological mechanisms that drive the observed biphasic decline in HBV DNA and RNA, and mechanistic differences between HBeAg-positive and negative infection. Finally, we demonstrate analytically and numerically that HBV RNA is more sensitive than HBV DNA to increases in CAM effectiveness.
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Affiliation(s)
- Sarafa A. Iyaniwura
- Theoretical Biology and Biophysics, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Tyler Cassidy
- School of Mathematics, University of Leeds, Leeds, LS2 9JT, United Kingdom
| | - Ruy M. Ribeiro
- Theoretical Biology and Biophysics, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Alan S. Perelson
- Theoretical Biology and Biophysics, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
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3
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Cassidy T. A Continuation Technique for Maximum Likelihood Estimators in Biological Models. Bull Math Biol 2023; 85:90. [PMID: 37650951 PMCID: PMC10471725 DOI: 10.1007/s11538-023-01200-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 08/14/2023] [Indexed: 09/01/2023]
Abstract
Estimating model parameters is a crucial step in mathematical modelling and typically involves minimizing the disagreement between model predictions and experimental data. This calibration data can change throughout a study, particularly if modelling is performed simultaneously with the calibration experiments, or during an on-going public health crisis as in the case of the COVID-19 pandemic. Consequently, the optimal parameter set, or maximal likelihood estimator (MLE), is a function of the experimental data set. Here, we develop a numerical technique to predict the evolution of the MLE as a function of the experimental data. We show that, when considering perturbations from an initial data set, our approach is significantly more computationally efficient that re-fitting model parameters while producing acceptable model fits to the updated data. We use the continuation technique to develop an explicit functional relationship between fit model parameters and experimental data that can be used to measure the sensitivity of the MLE to experimental data. We then leverage this technique to select between model fits with similar information criteria, a priori determine the experimental measurements to which the MLE is most sensitive, and suggest additional experiment measurements that can resolve parameter uncertainty.
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Affiliation(s)
- Tyler Cassidy
- School of Mathematics, University of Leeds, Leeds, LS2 9JT, UK.
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Maini PK, Chaplain MAJ, Lewis MA, Sherratt JA. Special Collection: Celebrating J.D. Murray's Contributions to Mathematical Biology. Bull Math Biol 2021; 84:13. [PMID: 34865189 DOI: 10.1007/s11538-021-00955-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Philip K Maini
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, OX2 6GG, UK.
| | - Mark A J Chaplain
- School of Mathematics and Statistics, Mathematical Institute, University of St Andrews, St Andrews, KY16 9SS, UK
| | - Mark A Lewis
- Department of Mathematical and Statistical Sciences, CAB 545B, University of Alberta, Edmonton, AB, T6G 2G1, Canada
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5
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Cassidy T, Nichol D, Robertson-Tessi M, Craig M, Anderson ARA. The role of memory in non-genetic inheritance and its impact on cancer treatment resistance. PLoS Comput Biol 2021; 17:e1009348. [PMID: 34460809 PMCID: PMC8432806 DOI: 10.1371/journal.pcbi.1009348] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 09/10/2021] [Accepted: 08/11/2021] [Indexed: 12/24/2022] Open
Abstract
Intra-tumour heterogeneity is a leading cause of treatment failure and disease progression in cancer. While genetic mutations have long been accepted as a primary mechanism of generating this heterogeneity, the role of phenotypic plasticity is becoming increasingly apparent as a driver of intra-tumour heterogeneity. Consequently, understanding the role of this plasticity in treatment resistance and failure is a key component of improving cancer therapy. We develop a mathematical model of stochastic phenotype switching that tracks the evolution of drug-sensitive and drug-tolerant subpopulations to clarify the role of phenotype switching on population growth rates and tumour persistence. By including cytotoxic therapy in the model, we show that, depending on the strategy of the drug-tolerant subpopulation, stochastic phenotype switching can lead to either transient or permanent drug resistance. We study the role of phenotypic heterogeneity in a drug-resistant, genetically homogeneous population of non-small cell lung cancer cells to derive a rational treatment schedule that drives population extinction and avoids competitive release of the drug-tolerant sub-population. This model-informed therapeutic schedule results in increased treatment efficacy when compared against periodic therapy, and, most importantly, sustained tumour decay without the development of resistance.
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Affiliation(s)
- Tyler Cassidy
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Daniel Nichol
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, United Kingdom
| | - Mark Robertson-Tessi
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center, Tampa, Florida, United States of America
| | - Morgan Craig
- Département de mathématiques et de statistique, Université de Montréal, Montreal, Canada
- CHU Sainte-Justine, Montreal, Canada
| | - Alexander R. A. Anderson
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center, Tampa, Florida, United States of America
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Jenner AL, Aogo RA, Alfonso S, Crowe V, Deng X, Smith AP, Morel PA, Davis CL, Smith AM, Craig M. COVID-19 virtual patient cohort suggests immune mechanisms driving disease outcomes. PLoS Pathog 2021; 17:e1009753. [PMID: 34260666 PMCID: PMC8312984 DOI: 10.1371/journal.ppat.1009753] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 07/26/2021] [Accepted: 06/24/2021] [Indexed: 12/11/2022] Open
Abstract
To understand the diversity of immune responses to SARS-CoV-2 and distinguish features that predispose individuals to severe COVID-19, we developed a mechanistic, within-host mathematical model and virtual patient cohort. Our results suggest that virtual patients with low production rates of infected cell derived IFN subsequently experienced highly inflammatory disease phenotypes, compared to those with early and robust IFN responses. In these in silico patients, the maximum concentration of IL-6 was also a major predictor of CD8+ T cell depletion. Our analyses predicted that individuals with severe COVID-19 also have accelerated monocyte-to-macrophage differentiation mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings suggest biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation.
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Affiliation(s)
- Adrianne L. Jenner
- Sainte-Justine University Hospital Research Centre, Montréal, Québec, Canada
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Québec, Canada
| | - Rosemary A. Aogo
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
| | - Sofia Alfonso
- Department of Physiology, McGill University, Montréal, Québec, Canada
| | - Vivienne Crowe
- Department of Mathematics and Statistics, Concordia University, Montréal, Québec, Canada
| | - Xiaoyan Deng
- Sainte-Justine University Hospital Research Centre, Montréal, Québec, Canada
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Québec, Canada
| | - Amanda P. Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
| | - Penelope A. Morel
- Department of Immunology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Courtney L. Davis
- Natural Science Division, Pepperdine University, Malibu, California, United States of America
| | - Amber M. Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
| | - Morgan Craig
- Sainte-Justine University Hospital Research Centre, Montréal, Québec, Canada
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Québec, Canada
- Department of Physiology, McGill University, Montréal, Québec, Canada
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7
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Shah T, Baloch Z, Shah Z, Cui X, Xia X. The Intestinal Microbiota: Impacts of Antibiotics Therapy, Colonization Resistance, and Diseases. Int J Mol Sci 2021; 22:ijms22126597. [PMID: 34202945 PMCID: PMC8235228 DOI: 10.3390/ijms22126597] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/08/2021] [Accepted: 06/14/2021] [Indexed: 12/11/2022] Open
Abstract
Trillions of microbes exist in the human body, particularly the gastrointestinal tract, coevolved with the host in a mutually beneficial relationship. The main role of the intestinal microbiome is the fermentation of non-digestible substrates and increased growth of beneficial microbes that produce key antimicrobial metabolites such as short-chain fatty acids, etc., to inhibit the growth of pathogenic microbes besides other functions. Intestinal microbiota can prevent pathogen colonization through the mechanism of colonization resistance. A wide range of resistomes are present in both beneficial and pathogenic microbes. Giving antibiotic exposure to the intestinal microbiome (both beneficial and hostile) can trigger a resistome response, affecting colonization resistance. The following review provides a mechanistic overview of the intestinal microbiome and the impacts of antibiotic therapy on pathogen colonization and diseases. Further, we also discuss the epidemiology of immunocompromised patients who are at high risk for nosocomial infections, colonization and decolonization of multi-drug resistant organisms in the intestine, and the direct and indirect mechanisms that govern colonization resistance to the pathogens.
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Affiliation(s)
- Taif Shah
- Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming 650500, China;
- Yunnan Key Laboratory of Sustainable Utilization of Panax Notoginseng, Kunming 650500, China
| | - Zulqarnain Baloch
- Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming 650500, China;
- Correspondence: (Z.B.); (X.C.); (X.X.)
| | - Zahir Shah
- Faculty of Animal Husbandry and Veterinary Sciences, College of Veterinary Sciences, The University of Agriculture Peshawar, Peshawar 25120, Pakistan;
| | - Xiuming Cui
- Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming 650500, China;
- Yunnan Key Laboratory of Sustainable Utilization of Panax Notoginseng, Kunming 650500, China
- Correspondence: (Z.B.); (X.C.); (X.X.)
| | - Xueshan Xia
- Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming 650500, China;
- Correspondence: (Z.B.); (X.C.); (X.X.)
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Jenner AL, Aogo RA, Alfonso S, Crowe V, Smith AP, Morel PA, Davis CL, Smith AM, Craig M. COVID-19 virtual patient cohort reveals immune mechanisms driving disease outcomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2021.01.05.425420. [PMID: 33442689 PMCID: PMC7805446 DOI: 10.1101/2021.01.05.425420] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
To understand the diversity of immune responses to SARS-CoV-2 and distinguish features that predispose individuals to severe COVID-19, we developed a mechanistic, within-host mathematical model and virtual patient cohort. Our results indicate that virtual patients with low production rates of infected cell derived IFN subsequently experienced highly inflammatory disease phenotypes, compared to those with early and robust IFN responses. In these in silico patients, the maximum concentration of IL-6 was also a major predictor of CD8 + T cell depletion. Our analyses predicted that individuals with severe COVID-19 also have accelerated monocyte-to-macrophage differentiation that was mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings identify biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation. AUTHOR SUMMARY Understanding of how the immune system responds to SARS-CoV-2 infections is critical for improving diagnostic and treatment approaches. Identifying which immune mechanisms lead to divergent outcomes can be clinically difficult, and experimental models and longitudinal data are only beginning to emerge. In response, we developed a mechanistic, mathematical and computational model of the immunopathology of COVID-19 calibrated to and validated against a broad set of experimental and clinical immunological data. To study the drivers of severe COVID-19, we used our model to expand a cohort of virtual patients, each with realistic disease dynamics. Our results identify key processes that regulate the immune response to SARS-CoV-2 infection in virtual patients and suggest viable therapeutic targets, underlining the importance of a rational approach to studying novel pathogens using intra-host models.
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Affiliation(s)
- Adrianne L. Jenner
- CHU Sainte-Justine Research Centre, Montréal, Québec, Canada
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Québec, Canada
| | - Rosemary A. Aogo
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Sofia Alfonso
- Department of Physiology, McGill University, Montréal, Québec, Canada
| | - Vivienne Crowe
- Department of Mathematics and Statistics, Concordia University, Montréal, Québec, Canada
| | - Amanda P. Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Penelope A. Morel
- Department of Immunology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Courtney L. Davis
- Natural Science Division, Pepperdine University, Malibu, California, USA
| | - Amber M. Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Morgan Craig
- CHU Sainte-Justine Research Centre, Montréal, Québec, Canada
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Québec, Canada
- Department of Physiology, McGill University, Montréal, Québec, Canada
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