Dataset Information

Caspases and therapeutic potential of caspase inhibitors in moderate-severe SARS-CoV-2 infection and long COVID.

ABSTRACT:

Background

COVID-19 can present with lymphopenia and extraordinary complex multiorgan pathologies that can trigger long-term sequela.

Aims

Given that inflammasome products, like caspase-1, play a role in the pathophysiology of a number of co-morbid conditions, we investigated caspases across the spectrum of COVID-19 disease.

Materials & methods

We assessed transcriptional states of multiple caspases and using flow cytometry, the expression of active caspase-1 in blood cells from COVID-19 patients in acute and convalescent stages of disease. Non-COVID-19 subject presenting with various comorbid conditions served as controls.

Results

Single-cell RNA-seq data of immune cells from COVID-19 patients showed a distinct caspase expression pattern in T cells, neutrophils, dendritic cells, and eosinophils compared with controls. Caspase-1 was upregulated in CD4+ T-cells from hospitalized COVID-19 patients compared with unexposed controls. Post-COVID-19 patients with lingering symptoms (long-haulers) also showed upregulated caspase-1activity in CD4+ T-cells that ex vivo was attenuated with a select pan-caspase inhibitor. We observed elevated caspase-3/7levels in red blood cells from COVID-19 patients compared with controls that was reduced following caspase inhibition.

Discussion

Our preliminary results suggest an exuberant caspase response in COVID-19 that may facilitate immune-related pathological processes leading to severe outcomes. Further clinical correlations of caspase expression in different stages of COVID-19 will be needed.

Conclusion

Pan-caspase inhibition could emerge as a therapeutic strategy to ameliorate or prevent severe COVID-19.

SUBMITTER: Plassmeyer M

PROVIDER: S-EPMC8222863 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background and Objectives: The severe forms of SARS-CoV-2 pneumonia are associated with acute hypoxic respiratory failure and high mortality rates, raising significant challenges for the medical community. The objective of this paper is to present the importance of early quantitative evaluation of radiological changes in SARS-CoV-2 pneumonia, including an alternative way to evaluate lung involvement using normal density clusters. Based on these elements we have developed a more accurate new predictive score which includes quantitative radiological parameters. The current evolution models used in the evaluation of severe cases of COVID-19 only include qualitative or semi-quantitative evaluations of pulmonary lesions which lead to a less accurate prognosis and assessment of pulmonary involvement. Materials and Methods: We performed a retrospective observational cohort study that included 100 adult patients admitted with confirmed severe COVID-19. The patients were divided into two groups: group A (76 survivors) and group B (24 non-survivors). All patients were evaluated by CT scan upon admission in to the hospital. Results: We found a low percentage of normal lung densities, PaO2/FiO2 ratio, lymphocytes, platelets, hemoglobin and serum albumin associated with higher mortality; a high percentage of interstitial lesions, oxygen flow, FiO2, Neutrophils/lymphocytes ratio, lactate dehydrogenase, creatine kinase MB, myoglobin, and serum creatinine were also associated with higher mortality. The most accurate regression model included the predictors of age, lymphocytes, PaO2/FiO2 ratio, percent of lung involvement, lactate dehydrogenase, serum albumin, D-dimers, oxygen flow, and myoglobin. Based on these parameters we developed a new score (COV-Score). Conclusions: Quantitative assessment of lung lesions improves the prediction algorithms compared to the semi-quantitative parameters. The cluster evaluation algorithm increases the non-survivor and overall prediction accuracy.COV-Score represents a viable alternative to current prediction scores, demonstrating improved sensitivity and specificity in predicting mortality at the time of admission.