โก Quick Summary
This review explores the complex dynamics of sepsis, highlighting how systemic toxicity leads to multi-organ injury. By integrating multi-omics and advanced computational modeling, the study identifies critical toxic nodes and offers insights into potential therapeutic strategies.
๐ Key Details
- ๐ Study Period: 2015 to 2025
- ๐ Sources: PubMed, Web of Science, Scopus
- ๐ฌ Focus: Mechanistic studies on sepsis
- โ๏ธ Technologies: Dynamic Bayesian networks (DBN), Graph neural networks (GNN)
- ๐งฌ Key Targets: NF-ฮบB, NLRP3 inflammasome, GPX4
๐ Key Takeaways
- ๐ Sepsis is a systemic disease driven by inflammation and immune dysregulation.
- ๐ Signaling pathways evolve over time, transitioning from pro-inflammatory to immunosuppressive states.
- ๐งฌ Inter-organ communication is mediated by damage-associated molecular patterns (DAMPs) and metabolites.
- โก Oxidative stress and mitochondrial dysfunction reinforce toxicity loops through pyroptosis and ferroptosis.
- ๐ค AI technologies are being developed for personalized toxicity maps and early-warning systems.
- ๐ Multi-omics integration can help identify critical nodes in the sepsis toxicity network.
- ๐ ๏ธ Challenges remain in specificity, safety, and resistance in therapeutic approaches.
- ๐ The framework offers a path toward individualized, mechanism-based care for sepsis.
๐ Background
Sepsis is a life-threatening condition characterized by a dysregulated immune response to infection, leading to systemic inflammation and multi-organ dysfunction. Understanding the underlying mechanisms of sepsis is crucial for developing effective treatments. Recent advancements in multi-omics and computational modeling provide new avenues for exploring the complex interactions within the sepsis toxicity network.
๐๏ธ Study
This narrative review synthesizes findings from various mechanistic studies published between 2015 and 2025. The authors focused on key organs affected by sepsis, including the lungs, liver, kidneys, and heart, to understand how signaling pathways evolve and interact. The review emphasizes the importance of integrating multi-omics data and advanced computational techniques to identify critical toxic nodes and predict disease progression.
๐ Results
The evidence presented in the review illustrates a high-dimensional systemic network that remodels over time. Early pro-inflammatory responses transition into patterns of immunosuppression and organ-specific injuries. The use of DBN and GNN modeling has been pivotal in delineating regulatory hubs within this network, supporting the forecasting of disease progression and potential therapeutic targets.
๐ Impact and Implications
The insights gained from this review have significant implications for the management of sepsis. By understanding the dynamic signaling pathways and integrating advanced computational approaches, healthcare providers can develop more effective, individualized treatment strategies. This could lead to improved patient outcomes and a better understanding of the complex nature of sepsis as a systemic toxicity network.
๐ฎ Conclusion
The reconstruction of the sepsis toxicity network through multi-omics and computational modeling represents a promising frontier in sepsis research. This framework not only enhances our understanding of the disease but also paves the way for personalized, mechanism-based care. Continued research and validation are essential to ensure the clinical applicability of these findings and to improve the management of sepsis in diverse patient populations.
๐ฌ Your comments
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Sepsis toxicity network reconstruction-Dynamic signaling and multi-organ injury: A review.
Abstract
Sepsis is a complex systemic disease in which systemic toxicity-arising from inflammation-immune dysregulation, oxidative stress, programmed cell death (apoptosis, pyroptosis, ferroptosis), and metabolic reprogramming-drives multi-organ injury. The aim of this review was to synthesize how signaling pathways evolve within and between key organs (lungs, liver, kidneys, heart) and to evaluate whether multi-omics integration and network modeling can identify critical toxic nodes and predict disease progression. We conducted a narrative review of English-language mechanistic studies published between 2015 and 2025 in PubMed, Web of Science, and Scopus, supplemented by bibliography screening, while excluding case reports, conference abstracts, and non-mechanistic work. The evidence depicts a high-dimensional systemic network that remodels over time, with early pro-inflammatory modules transitioning toward immunosuppression and organ-specific injury patterns, while inter-organ propagation is mediated by damage-associated molecular patterns (DAMPs), exosomes, and metabolites. Oxidative stress and mitochondrial dysfunction, via reactive oxygen species (ROS), couple to pyroptosis and ferroptosis to reinforce toxicity loops, and computational approaches such as dynamic Bayesian networks (DBN) and graph neural networks (GNN) delineate regulatory hubs and support forecasting. Therapeutic progress has concentrated on nuclear factor kappa-light-chain-enhancer of activated B cells (NF-ฮบB), the NOD-, leucine-rich repeat and pyrin domain-containing protein 3 (NLRP3) inflammasome, and glutathione peroxidase 4 (GPX4), alongside artificial intelligence (AI)-assisted personalized toxicity maps and dynamic early-warning systems, though challenges remain in specificity, safety, and resistance. In conclusion, sepsis can be conceived as a temporally staged systemic toxicity network, and when combined with multi-omics, DBN/GNN modeling, and AI-enabled decision support, this framework offers a path toward individualized, mechanism-based care, while requiring rigorous validation to ensure clinical durability.
Author: [‘Liu S’, ‘Liang Q’]
Journal: Biomol Biomed
Citation: Liu S and Liang Q. Sepsis toxicity network reconstruction-Dynamic signaling and multi-organ injury: A review. Sepsis toxicity network reconstruction-Dynamic signaling and multi-organ injury: A review. 2025; (unknown volume):(unknown pages). doi: 10.17305/bb.2025.12931