Üstündağ, Hilal

Sepsis remains a leading cause of mortality in intensive care units worldwide, representing life-threatening organ dysfunction arising from dysregulated host responses to infection. The purinergic P2X7 receptor (P2X7R) and the NOD-like receptor pyrin domain-containing protein 3 (NLRP3) inflammasome have emerged as critical regulators of inflammatory responses during sepsis pathogenesis. Extracellular adenosine triphosphate (ATP), released as a danger-associated molecular pattern from damaged and dying cells, activates the P2X7R, triggering potassium efflux that serves as the cardinal signal for NLRP3 inflammasome assembly. The activated inflammasome complex mediates caspase-1-dependent proteolytic maturation of the proinflammatory cytokines interleukin-1β (IL-1β) and IL-18, while simultaneously inducing gasdermin D (GSDMD)-mediated pyroptotic cell death—an inflammatory form of programmed cell death characterized by membrane pore formation, cellular swelling, and release of intracellular contents. Paradoxically, emerging evidence indicates that excessive P2X7R activation during sepsis progression induces profound mitochondrial dysfunction in circulating monocytes, leading to impaired NLRP3 inflammasome function and a state of immunoparalysis that coincides with an increased mortality. This comprehensive review examines the molecular mechanisms governing P2X7/NLRP3 signaling in sepsis; critically evaluates its dual and seemingly contradictory roles in hyperinflammation versus immunosuppression; delineates organ-specific manifestations across the lung, kidney, heart, brain, liver, and intestine; and systematically evaluates emerging therapeutic strategies including direct NLRP3 inhibitors such as MCC950, selective P2X7R antagonists, IL-1 receptor blockade, GSDMD inhibitors, and natural compounds with inflammasome-modulating properties. Understanding the temporal dynamics of this signaling axis is crucial for developing precision-targeted interventions that appropriately balance pathogen clearance with prevention of excessive inflammatory tissue damage.

Ustundag, Hilal; Doganay, Songul

Sepsis-induced multi-organ dysfunction syndrome (MODS) remains a leading cause of intensive care unit (ICU) mortality worldwide. The complex interplay among inflammatory signaling pathways TLR4/NF-κB, NLRP3/P2X7 and Nrf2/HO-1 plays a critical role in determining organ damage trajectories, yet existing artificial intelligence (AI) models fail to capture these molecular dynamics. We propose PathSepsisNet, a pathway-aware temporal graph attention network that integrates biological pathway crosstalk knowledge into a deep learning architecture for early MODS prediction. We developed a biologically-informed graph neural network in which six nodes represent molecular pathway activation states (estimated from routine clinical biomarkers) and twelve directed edges encode established crosstalk relationships. A dual spatial–temporal attention mechanism with gated fusion captures inter-pathway crosstalk dynamics and within-pathway temporal evolution simultaneously. A novel Pathway Divergence Score (PDS) quantifies the imbalance between pro-inflammatory and cytoprotective pathways. The model was trained and validated on 29,765 adult patients with sepsis (Sepsis-3) from the Medical Information Mart for Intensive Care IV (MIMIC-IV) v3.1 using only the first 12 h of ICU data to predict MODS development at 12-36 h, with strict data leakage prevention (no SOFA sub-scores as inputs). PathSepsisNet achieved area under the receiver operating characteristic curve (AUROC) 0.895 and an area under the precision-recall curve (AUPRC) of 0.901 for MODS prediction, outperforming bidirectional long short term memory (BiLSTM) (0.886), Transformer (0.890) and logistic regression (0.876), while providing unique pathway-level interpretability. Gradient boosting methods (XGBoost: 0.906; LightGBM: 0.905) achieved marginally higher raw AUROC, but lack mechanistic insight. The PDS demonstrated remarkable standalone discriminative ability (MODS + : 0.596 ± 0.185 vs. MODS − : 0.299 ± 0.163; PDS AUROC: 0.872). Node importance analysis identified CytokineStorm (0.290), NLRP3/P2X7 (0.212) and mitochondrial dysfunction (0.211) as the most influential pathway modules. MODS + patients exhibited significantly higher lactate (2.34 vs. 1.63 mmol/L), CRP (123.5 vs. 97.2 mg/L), bilirubin (2.91 vs. 0.97 mg/dL) and lower albumin (2.82 vs. 3.04 g/dL) and platelets (160 vs. 207 K/μL), validating pathway-specific hypotheses. PathSepsisNet demonstrates that integrating biological pathway knowledge into graph neural network architectures yields predictive performance competitive with state-of-the-art methods, while uniquely providing pathway-level mechanistic insights. The PDS represents a novel computational biomarker bridging molecular pathway biology and clinical decision support. These findings support the paradigm that biologically informed AI can close the gap between predictive accuracy and clinical interpretability in sepsis management.