Metabolic dysregulation impairs lymphocyte function during severe SARS-CoV-2 infection.

Cellular metabolic dysregulation is a consequence of SARS-CoV-2 infection that is a key determinant of disease severity. However, how metabolic perturbations influence immunological function during COVID-19 remains unclear. Here, using a combination of high-dimensional flow cytometry, cutting-edge single-cell metabolomics, and re-analysis of single-cell transcriptomic data, we demonstrate a global hypoxia-linked metabolic switch from fatty acid oxidation and mitochondrial respiration towards anaerobic, glucose-dependent metabolism in CD8+Tc, NKT, and epithelial cells. Consequently, we found that a strong dysregulation in immunometabolism was tied to increased cellular exhaustion, attenuated effector function, and impaired memory differentiation. Pharmacological inhibition of mitophagy with mdivi-1 reduced excess glucose metabolism, resulting in enhanced generation of SARS-CoV-2- specific CD8+Tc, increased cytokine secretion, and augmented memory cell proliferation. Taken together, our study provides critical insight regarding the cellular mechanisms underlying the effect of SARS-CoV-2 infection on host immune cell metabolism, and highlights immunometabolism as a promising therapeutic target for COVID-19 treatment.

Hypothesis-Agnostic Network-Based Analysis of Real-World Data Suggests Ondansetron is Associated with Lower COVID-19 Any Cause Mortality.

BACKGROUND: The COVID-19 pandemic generated a massive amount of clinical data, which potentially hold yet undiscovered answers related to COVID-19 morbidity, mortality, long-term effects, and therapeutic solutions. OBJECTIVES: The objectives of this study were (1) to identify novel predictors of COVID-19 any cause mortality by employing artificial intelligence analytics on real-world data through a hypothesis-agnostic approach and (2) to determine if these effects are maintained after adjusting for potential confounders and to what degree they are moderated by other variables. METHODS: A Bayesian statistics-based artificial intelligence data analytics tool (bAIcis®) within the Interrogative Biology® platform was used for Bayesian network learning and hypothesis generation to analyze 16,277 PCR+ patients from a database of 279,281 inpatients and outpatients tested for SARS-CoV-2 infection by antigen, antibody, or PCR methods during the first pandemic year in Central Florida. This approach generated Bayesian networks that enabled unbiased identification of significant predictors of any cause mortality for specific COVID-19 patient populations. These findings were further analyzed by logistic regression, regression by least absolute shrinkage and selection operator, and bootstrapping. RESULTS: We found that in the COVID-19 PCR+ patient cohort, early use of the antiemetic agent ondansetron was associated with decreased any cause mortality 30 days post-PCR+ testing in mechanically ventilated patients. CONCLUSIONS: The results demonstrate how a real-world COVID-19-focused data analysis using artificial intelligence can generate unexpected yet valid insights that could possibly support clinical decision making and minimize the future loss of lives and resources.

Understanding the Interaction between Type 2 Diabetes and COVID-via Extracellular Vesicle Proteomic and Phosphoproteomic Signatures

Evidence supporting the involvement of EVs in the pathogenesis/severity of SARS-CoV-2 infection is starting to accumulate. However, little is known about their specific associations in the context of COVID-and type 2 diabetes interaction. Our study included 48 plasma samples (N=12/group) obtained from COVID-patients with and without diabetes and from patients with non-COVID-acute respiratory diagnosis (RSP) with and without diabetes. Participants were identified from a set of 494 patients hospitalized at AdventHealth in June-August 2020. Important efforts were made to ensure the homogeneity of the study cohort. Patients with type 1 diabetes, or pregnant, or that went directly into the ICU were excluded, and 4 balanced groups were identified after 10,000 random cohorts were generated and differences in age, gender, race, and ethnicity statistically assessed. EVs were isolated using EVTRAP (Tymora) . Mass spectrometry-based methods were used to detect the global EV proteome and phosphoproteome. Differentially expressed features, enriched pathways, and enriched tissue-specific protein sets were identified. Multidimensional scaling of all EV proteomic and phosphoproteomic data and unsupervised clustering of differentially expressed (absolute fold change ≥ 2, P < 0.05, FDR < 0.05) EV proteins and phosphoproteins successfully distinguished the 4 study groups with close to 100% accuracy. Importantly, we detected enriched pathway networks that suggest the potential therapeutic utility of PKC inhibitors such as bisindolylmaleimide IX, sotrastaurin, and enzastaumn, and inhibitors of ROCK1 such the isoquinoline derivative Fasudil. In conclusion, we characterized the proteomic landscape of the interaction between type 2 diabetes and COVID-and defined disease-specific EV proteomic signatures that provide insight into the disease pathobiology and druggable targets with potential clinical utility.