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Shankar-Hari M, Calandra T, Soares MP, Bauer M, Wiersinga WJ, Prescott HC, Knight JC, Baillie KJ, Bos LDJ, Derde LPG, Finfer S, Hotchkiss RS, Marshall J, Openshaw PJM, Seymour CW, Venet F, Vincent JL, Le Tourneau C, Maitland-van der Zee AH, McInnes IB, van der Poll T. Reframing sepsis immunobiology for translation: towards informative subtyping and targeted immunomodulatory therapies. THE LANCET. RESPIRATORY MEDICINE 2024; 12:323-336. [PMID: 38408467 PMCID: PMC11025021 DOI: 10.1016/s2213-2600(23)00468-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 11/27/2023] [Accepted: 12/07/2023] [Indexed: 02/28/2024]
Abstract
Sepsis is a common and deadly condition. Within the current model of sepsis immunobiology, the framing of dysregulated host immune responses into proinflammatory and immunosuppressive responses for the testing of novel treatments has not resulted in successful immunomodulatory therapies. Thus, the recent focus has been to parse observable heterogeneity into subtypes of sepsis to enable personalised immunomodulation. In this Personal View, we highlight that many fundamental immunological concepts such as resistance, disease tolerance, resilience, resolution, and repair are not incorporated into the current sepsis immunobiology model. The focus for addressing heterogeneity in sepsis should be broadened beyond subtyping to encompass the identification of deterministic molecular networks or dominant mechanisms. We explicitly reframe the dysregulated host immune responses in sepsis as altered homoeostasis with pathological disruption of immune-driven resistance, disease tolerance, resilience, and resolution mechanisms. Our proposal highlights opportunities to identify novel treatment targets and could enable successful immunomodulation in the future.
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Affiliation(s)
- Manu Shankar-Hari
- Institute for Regeneration and Repair, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, UK.
| | - Thierry Calandra
- Service of Immunology and Allergy, Center of Human Immunology Lausanne, Department of Medicine and Department of Laboratory Medicine and Pathology, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | | | - Michael Bauer
- Center for Sepsis Control and Care, Jena University Hospital, Jena, Germany
| | - W Joost Wiersinga
- Center for Experimental and Molecular Medicine and Division of Infectious Diseases, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Hallie C Prescott
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Julian C Knight
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Kenneth J Baillie
- Institute for Regeneration and Repair, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, UK
| | - Lieuwe D J Bos
- Department of Intensive Care, Academic Medical Center, Amsterdam, Netherlands
| | - Lennie P G Derde
- Intensive Care Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Simon Finfer
- Critical Care Division, The George Institute for Global Health, University of New South Wales, Sydney, NSW, Australia
| | - Richard S Hotchkiss
- Department of Anesthesiology and Critical Care Medicine, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - John Marshall
- Interdepartmental Division of Critical Care, University of Toronto, Toronto, ON, Canada
| | | | - Christopher W Seymour
- Department of Critical Care Medicine, The Clinical Research, Investigation, and Systems Modeling of Acute illness (CRISMA) Center, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Fabienne Venet
- Immunology Laboratory, Edouard Herriot Hospital, Hospices Civils de Lyon, Lyon, France
| | | | - Christophe Le Tourneau
- Department of Drug Development and Innovation (D3i), Institut Curie, Paris-Saclay University, Paris, France
| | - Anke H Maitland-van der Zee
- Department of Pulmonary Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Iain B McInnes
- College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Tom van der Poll
- Center for Experimental and Molecular Medicine and Division of Infectious Diseases, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
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Nanda P, Kirschner DE. Calibration methods to fit parameters within complex biological models. FRONTIERS IN APPLIED MATHEMATICS AND STATISTICS 2023; 9:1256443. [PMID: 38222943 PMCID: PMC10785782 DOI: 10.3389/fams.2023.1256443] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Mathematical and computational models of biological systems are increasingly complex, typically comprised of hybrid multi-scale methods such as ordinary differential equations, partial differential equations, agent-based and rule-based models, etc. These mechanistic models concurrently simulate detail at resolutions of whole host, multi-organ, organ, tissue, cellular, molecular, and genomic dynamics. Lacking analytical and numerical methods, solving complex biological models requires iterative parameter sampling-based approaches to establish appropriate ranges of model parameters that capture corresponding experimental datasets. However, these models typically comprise large numbers of parameters and therefore large degrees of freedom. Thus, fitting these models to multiple experimental datasets over time and space presents significant challenges. In this work we undertake the task of reviewing, testing, and advancing calibration practices across models and dataset types to compare methodologies for model calibration. Evaluating the process of calibrating models includes weighing strengths and applicability of each approach as well as standardizing calibration methods. Our work compares the performance of our model agnostic Calibration Protocol (CaliPro) with approximate Bayesian computing (ABC) to highlight strengths, weaknesses, synergies, and differences among these methods. We also present next-generation updates to CaliPro. We explore several model implementations and suggest a decision tree for selecting calibration approaches to match dataset types and modeling constraints.
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Affiliation(s)
- Pariksheet Nanda
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Denise E. Kirschner
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, United States
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Rychkov D, Neely J, Oskotsky T, Yu S, Perlmutter N, Nititham J, Carvidi A, Krueger M, Gross A, Criswell LA, Ashouri JF, Sirota M. Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis. Front Immunol 2021; 12:638066. [PMID: 34177888 PMCID: PMC8223752 DOI: 10.3389/fimmu.2021.638066] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 05/17/2021] [Indexed: 01/20/2023] Open
Abstract
There is an urgent need to identify biomarkers for diagnosis and disease activity monitoring in rheumatoid arthritis (RA). We leveraged publicly available microarray gene expression data in the NCBI GEO database for whole blood (N=1,885) and synovial (N=284) tissues from RA patients and healthy controls. We developed a robust machine learning feature selection pipeline with validation on five independent datasets culminating in 13 genes: TNFAIP6, S100A8, TNFSF10, DRAM1, LY96, QPCT, KYNU, ENTPD1, CLIC1, ATP6V0E1, HSP90AB1, NCL and CIRBP which define the RA score and demonstrate its clinical utility: the score tracks the disease activity DAS28 (p = 7e-9), distinguishes osteoarthritis (OA) from RA (OR 0.57, p = 8e-10) and polyJIA from healthy controls (OR 1.15, p = 2e-4) and monitors treatment effect in RA (p = 2e-4). Finally, the immunoblotting analysis of six proteins on an independent cohort confirmed two proteins, TNFAIP6/TSG6 and HSP90AB1/HSP90.
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Affiliation(s)
- Dmitry Rychkov
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, United States
- Department of Surgery, University of California San Francisco, San Francisco, CA, United States
- Department of Pediatrics, University of California San Francisco, San Francisco, CA, United States
| | - Jessica Neely
- Department of Pediatrics, University of California San Francisco, San Francisco, CA, United States
| | - Tomiko Oskotsky
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, United States
| | - Steven Yu
- Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center, Division of Rheumatology, Department of Medicine, University of California San Francisco, San Francisco, CA, United States
- Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA, United States
| | - Noah Perlmutter
- Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center, Division of Rheumatology, Department of Medicine, University of California San Francisco, San Francisco, CA, United States
| | - Joanne Nititham
- Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center, Division of Rheumatology, Department of Medicine, University of California San Francisco, San Francisco, CA, United States
| | - Alexander Carvidi
- Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center, Division of Rheumatology, Department of Medicine, University of California San Francisco, San Francisco, CA, United States
| | - Melissa Krueger
- Department of Medicine, Oregon Health & Science University, Portland, OR, United States
| | - Andrew Gross
- Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center, Division of Rheumatology, Department of Medicine, University of California San Francisco, San Francisco, CA, United States
| | - Lindsey A. Criswell
- Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center, Division of Rheumatology, Department of Medicine, University of California San Francisco, San Francisco, CA, United States
- Institute for Human Genetics (IHG), University of California San Francisco, San Francisco, CA, United States
- Department of Medicine, University of California San Francisco, San Francisco, CA, United States
- Department of Orofacial Sciences, University of California San Francisco, San Francisco, CA, United States
| | - Judith F. Ashouri
- Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center, Division of Rheumatology, Department of Medicine, University of California San Francisco, San Francisco, CA, United States
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, United States
- Department of Pediatrics, University of California San Francisco, San Francisco, CA, United States
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