Damage-associated molecular patterns (DAMPs) are endogenous molecules which foment inflammation and are associated with disorders in sepsis and cancer. Thus, therapeutically targeting DAMPs has potential to provide novel and effective treatments. When establishing anti-DAMP strategies, it is important not only to focus on the DAMPs as inflammatory mediators but also to take into account the underlying mechanisms of their release from cells and tissues. DAMPs can be released passively by membrane rupture due to necrosis/necroptosis, although the mechanisms of release appear to differ between the DAMPs. Other types of cell death, such as apoptosis, pyroptosis, ferroptosis and NETosis, can also contribute to DAMP release. In addition, some DAMPs can be exported actively from live cells by exocytosis of secretory lysosomes or exosomes, ectosomes, and activation of cell membrane channel pores. Here we review the shared and DAMP-specific mechanisms reported in the literature for high mobility group box 1, ATP, extracellular cold-inducible RNA-binding protein, histones, heat shock proteins, extracellular RNAs and cell-free DNA.

Background: Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk for deterioration. Given the complexity of COVID-19, machine learning approaches may support clinical decision making for patients with this disease. Objective: Our objective is to derive a machine learning model that predicts respiratory failure within 48 hours of admission based on data from the emergency department. Methods: Data were collected from patients with COVID-19 who were admitted to Northwell Health acute care hospitals and were discharged, died, or spent a minimum of 48 hours in the hospital between March 1 and May 11, 2020. Of 11,525 patients, 933 (8.1%) were placed on invasive mechanical ventilation within 48 hours of admission. Variables used by the models included clinical and laboratory data commonly collected in the emergency department. We trained and validated three predictive models (two based on XGBoost and one that used logistic regression) using cross-hospital validation. We compared model performance among all three models as well as an established early warning score (Modified Early Warning Score) using receiver operating characteristic curves, precision-recall curves, and other metrics. Results: The XGBoost model had the highest mean accuracy (0.919; area under the curve=0.77), outperforming the other two models as well as the Modified Early Warning Score. Important predictor variables included the type of oxygen delivery used in the emergency department, patient age, Emergency Severity Index level, respiratory rate, serum lactate, and demographic characteristics. Conclusions: The XGBoost model had high predictive accuracy, outperforming other early warning scores. The clinical plausibility and predictive ability of XGBoost suggest that the model could be used to predict 48-hour respiratory failure in admitted patients with COVID-19.

Pulmonary fibrosis is a chronic debilitating condition characterized by progressive deposition of connective tissue, leading to a steady restriction of lung elasticity, a decline in lung function, and a median survival of 4.5 years. The leading causes of pulmonary fibrosis are inhalation of foreign particles (such as silicosis and pneumoconiosis), infections (such as post COVID-19), autoimmune diseases (such as systemic autoimmune diseases of the connective tissue), and idiopathic pulmonary fibrosis. The therapeutics currently available for pulmonary fibrosis only modestly slow the progression of the disease. This review is centered on the interplay of damage-associated molecular pattern (DAMP) molecules, Toll-like receptor 4 (TLR4), and inflammatory cytokines (such as TNF-α, IL-1β, and IL-17) as they contribute to the pathogenesis of pulmonary fibrosis, and the possible avenues to develop effective therapeutics that disrupt this interplay.

Psoriatic arthritis (PsA) is a chronic and erosive form of arthritis of unknown cause. We aimed to characterize the PsA phenotype using gene expression profiling and comparing it with healthy control subjects and patients rheumatoid arthritis (RA). Peripheral blood cells (PBCs) of 19 patients with active PsA and 19 age- and sex-matched control subjects were used in the analyses of PsA, with blood samples collected in PaxGene tubes. A significant alteration in the pattern of expression of 313 genes was noted in the PBCs of PsA patients on Affymetrix U133A arrays: 257 genes were expressed at reduced levels in PsA, and 56 genes were expressed at increased levels, compared with controls. Downregulated genes tended to cluster to certain chromosomal regions, including those containing the psoriasis susceptibility loci PSORS1 and PSORS2. Among the genes with the most significantly reduced expression were those involved in downregulation or suppression of innate and acquired immune responses, such as SIGIRR, STAT3, SHP1, IKBKB, IL-11RA, and TCF7, suggesting inappropriate control that favors proin-flammatory responses. Several members of the MAPK signaling pathway and tumor suppressor genes showed reduced expression. Three proinflammatory genes--S100A8, S100A12, and thioredoxin--showed increased expression. Logistic regression and recursive partitioning analysis determined that one gene, nucleoporin 62 kDa, could correctly classify all controls and 94.7% of the PsA patients. Using a dataset of 48 RA samples for comparison, the combination of two genes, MAP3K3 followed by CACNA1S, was enough to correctly classify all RA and PsA patients. Thus, PBC gene expression profiling identified a gene expression signature that differentiated PsA from RA, and PsA from controls. Several novel genes were differentially expressed in PsA and may prove to be diagnostic biomarkers or serve as new targets for the development of therapies.

Cold-inducible RNA-binding protein (CIRP), released into the circulation during sepsis, causes lung injury via an as yet unknown mechanism. Since endoplasmic reticulum (ER) stress is associated with acute lung injury (ALI), we hypothesized that CIRP causes ALI via induction of ER stress. To test this hypothesis, we studied the lungs of wild-type (WT) and CIRP knockout (KO) mice at 20 h after induction of sepsis by cecal ligation and puncture (CLP). WT mice had significantly more severe ALI than CIRP KO mice. Lung ER stress markers (BiP, pIRE1α, sXBP1, CHOP, cleaved caspase-12) were increased in septic WT mice, but not in septic CIRP KO mice. Effector pathways downstream from ER stress – apoptosis, NF-κB (p65), proinflammatory cytokines (IL-6, IL-1β), neutrophil chemoattractants (MIP-2, KC), neutrophil infiltration (MPO activity), lipid peroxidation (4-HNE), and nitric oxide (iNOS) – were significantly increased in WT mice, but only mildly elevated in CIRP KO mice. ER stress markers were increased in the lungs of healthy WT mice treated with recombinant murine CIRP, but not in the lungs of TLR4 KO mice. This suggests CIRP directly induces ER stress via TLR4 activation. In summary, CIRP induces lung ER stress and downstream responses to cause sepsis-associated ALI.