Challenging molecular dogmas in human sepsis using mathematical reasoning

• Published in: EBioMedicine Vol. 80; p. 104031
• Main Authors: Ghazal, Peter, Rodrigues, Patricia R.S., Chakraborty, Mallinath, Oruganti, Siva, Woolley, Thomas E.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.06.2022; Elsevier
• Subjects: Artificial Intelligence; Big-data; Cytokine Release Syndrome; Cytokine storm; Dormancy; Dysregulation; Genomics; Homeostasis; Host-response; Humans; Hyperinflammatory; Immune-paralysis; Immuno-metabolism; Immunosuppression; Infection; Inflammation; Innate-immune; Mathematical model; Multi-omics; Non-resolving; Review; Sepsis; Sepsis - diagnosis; Setpoint; Immune-paralysis; Hyperinflammatory; Host-response; Genomics; Immuno-metabolism; Homeostasis; Dysregulation; Setpoint; Inflammation; Cytokine storm; Multi-omics; Infection; Dormancy; Immunosuppression; Sepsis; Non-resolving; Innate-immune; Mathematical model; Artificial intelligence; Big-data
• ISSN: 2352-3964; 2352-3964
• DOI: 10.1016/j.ebiom.2022.104031

Sepsis is defined as a dysregulated host-response to infection, across all ages and pathogens. What defines a dysregulated state remains intensively researched but incompletely understood. Here, we dissect the meaning of this definition and its importance for the diagnosis and management of sepsis. We deliberate on pathophysiological features and dogmas that range from cytokine storms and immune paralysis to dormancy and altered homeostasis setpoints. Mathematical reasoning, used to test for plausibility, reveals three interlinked cardinal rules governing host-response trajectories in sepsis. Rule one highlights that the amplitude of the immune response while important is not sufficient and is strictly dependent on rule two, specifying bioenergetic capacity and are together dynamically driven by rule three, delineating stability and alterations in setpoints. We consider these rules and associated pathophysiological parameters for guiding data-science and artificial intelligence mining of multi-omics and big-data for improving the precision of diagnostic and therapeutic approaches to sepsis. PG funded by the European Regional Development Fund and Welsh Government (Ser Cymru programme – Project Sepsis).