Divergent COVID-19 Disease Trajectories Predicted by a DAMP-Centered Immune Network Model

Day, Judy; Park, Soo‐Jin; Ranard, Benjamin L.; Singh, Harinder; Chow, Carson C.; Vodovotz, Yoram

doi:10.3389/fimmu.2021.754127

Cited by 12 publications

(23 citation statements)

References 97 publications

(125 reference statements)

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“…Interestingly, a recent publication showed that the stimulation of human blood cells with SARS-CoV-2 proteins induced hallmarks of CR, including reduced proinflammatory cytokine production and T cell proliferation, and enhancement of phagocytosis and tissue remodeling 48 . Furthermore, there have been other studies which have shown through bioinformatic analysis and mathematical models that related biological responses dysregulated during severe Covid-19 infection overlap with that of all-cause sepsis 38 , 49 , 50 . The Mortality signature also captured 30-day mortality quite well, indicating that similar mechanisms arise during severe COVID-19 infection and all-cause sepsis when death is impending.…”

Section: Discussionmentioning

confidence: 99%

Predicting severity in COVID-19 disease using sepsis blood gene expression signatures

Baghela

Zhang³

et al. 2023

Sci Rep

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Severely-afflicted COVID-19 patients can exhibit disease manifestations representative of sepsis, including acute respiratory distress syndrome and multiple organ failure. We hypothesized that diagnostic tools used in managing all-cause sepsis, such as clinical criteria, biomarkers, and gene expression signatures, should extend to COVID-19 patients. Here we analyzed the whole blood transcriptome of 124 early (1–5 days post-hospital admission) and late (6–20 days post-admission) sampled patients with confirmed COVID-19 infections from hospitals in Quebec, Canada. Mechanisms associated with COVID-19 severity were identified between severity groups (ranging from mild disease to the requirement for mechanical ventilation and mortality), and established sepsis signatures were assessed for dysregulation. Specifically, gene expression signatures representing pathophysiological events, namely cellular reprogramming, organ dysfunction, and mortality, were significantly enriched and predictive of severity and lethality in COVID-19 patients. Mechanistic endotypes reflective of distinct sepsis aetiologies and therapeutic opportunities were also identified in subsets of patients, enabling prediction of potentially-effective repurposed drugs. The expression of sepsis gene expression signatures in severely-afflicted COVID-19 patients indicates that these patients should be classified as having severe sepsis. Accordingly, in severe COVID-19 patients, these signatures should be strongly considered for the mechanistic characterization, diagnosis, and guidance of treatment using repurposed drugs.

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Section: Discussionmentioning

confidence: 99%

Predicting severity in COVID-19 disease using sepsis blood gene expression signatures

Baghela

Zhang³

et al. 2023

Sci Rep

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“…Sepsis advances rapidly and the immune system is strongly involved, with 60% of the patients genome altered within 30 minutes of hospital admission 15 and cytokines and receptors linked to increased inflammation and innate immune response particularly affected 15 . IL6 and IL10 are pro- and antiinflammatory cytokines, that have been used as inflammation markers in several models 16–19 and shown to increase production rates of altered neutrophils and monocytes in-vitro 20 .…”

Section: Introductionmentioning

confidence: 99%

A dynamic inflammation model for neutrophil and monocyte responses in sepsis, trauma and surgery patient clusters

Browning

Couenne

Bordes

et al. 2023

Preprint

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Time series clustering is applied to inflammation and neutrophil cell development markers, CD16 and CD10, in sepsis, trauma and surgery patients and a dynamical model with an inflammation function, F, is used to represent their evolution over a two month period. Five patient clusters are identified, characterised and evaluated against medical assessment scores and the literature. A dynamical model for neutrophil and monocyte cell counts and maturity has been constructed based on mass balances and cell kinetics in both blood and bone marrow. Cell proliferation and flow rates, as well as expression of monocyte HLA-DR, depend on concentrations of pro- and anti- inflammatory cytokines, IL6 and IL10, via F. A good fit with the data is obtained for each cluster and the estimated parameters correlate to illness severity. The model is a potential tool for simulation of immunomodulatory therapies.

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“…In contrast to apoptosis, which does not elicit inflammation, necrosis causes the release of damage-associated molecular patterns (DAMPs) such as high mobility group box (HMGB)-1 from dead cells [11][12][13] . Therefore, it is possible that the alveolar necrosis during early disease stages and subsequent release of DAMPs may drive disease progression in COVID-19-associated ARDS [14][15][16][17] .…”

Section: Introductionmentioning

confidence: 99%

Early alveolar epithelial cell necrosis is a potential driver of COVID-19-induced acute respiratory distress syndrome

Tojo

Yamamoto

Tamada

et al. 2022

Preprint

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Rationale: Acute respiratory distress syndrome (ARDS) with COVID-19 is aggravated by hyperinflammatory responses even after passing the peak of viral load. However, the underlying mechanisms remain unclear. Objectives : Here, we assess whether alveolar epithelial cell necrosis and subsequent releases of damage associated molecular patterns (DAMPs) at an early disease stage aggravate ARDS with COVID-19 Methods: In patients with COVID-19 with and without ARDS and healthy adults, serum levels of the following were quantified: an epithelial total cell death marker, cytokeratin18-M65; an epithelial apoptosis marker, CK18-M30; HMGB-1; and alveolar epithelial and endothelial injury markers, sRAGE, angiopoietin-2, and surfactant protein-D. Molecular mechanisms of alveolar epithelial cell death and effects of HMGB-1 neutralization on alveolar tissue injury were assessed using a mouse model of COVID-19-induced ARDS. Measurements and main results: The levels of CK18-M65, CK18-M30, and alveolar tissue injury markers were elevated in early stages of ARDS. The median M30/M65 ratio, an epithelial apoptosis indicator, was 31.50% in patients with ARDS, a value significantly lower than that of non-ARDS patients or healthy subjects. Serum levels of HMGB-1, one of DAMPs released from necrotic cells, were also significantly elevated in ARDS versus non-ARDS patients. In a COVID-19-induced ARDS mouse model, alveolar epithelial cell necrosis involved two forms of programmed necrosis, necroptosis and pyroptosis. Finally, neutralization of HMGB-1 attenuated alveolar tissue injury in the mouse model. Conclusions: Necrosis, including necroptosis and pyroptosis, seems to be the primary form of alveolar epithelial cell death, and subsequent release of DAMPs is a potential driver of COVID-19-induced ARDS.

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Divergent COVID-19 Disease Trajectories Predicted by a DAMP-Centered Immune Network Model

Cited by 12 publications

References 97 publications

Predicting severity in COVID-19 disease using sepsis blood gene expression signatures

Predicting severity in COVID-19 disease using sepsis blood gene expression signatures

A dynamic inflammation model for neutrophil and monocyte responses in sepsis, trauma and surgery patient clusters

Early alveolar epithelial cell necrosis is a potential driver of COVID-19-induced acute respiratory distress syndrome

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