Saliva metabolomic profile of COVID-19 patients associates with disease severity.

INTRODUCTION: Coronavirus disease 2019 (COVID-19) is strongly linked to dysregulation of various molecular, cellular, and physiological processes that change abundance of different biomolecules including metabolites that may be ultimately used as biomarkers for disease progression and severity. It is important at early stage to readily distinguish those patients that are likely to progress to moderate and severe stages. OBJECTIVES: This study aimed to investigate the utility of saliva and plasma metabolomic profiles as a potential parameter for risk stratifying COVID-19 patients. METHOD: LC-MS/MS-based untargeted metabolomics were used to profile the changes in saliva and plasma metabolomic profiles of COVID-19 patients with different severities. RESULTS: Saliva and plasma metabolites were screened in 62 COVID-19 patients and 18 non-infected controls. The COVID-19 group included 16 severe, 15 moderate, 16 mild, and 15 asymptomatic cases. Thirty-six differential metabolites were detected in COVID-19 versus control comparisons. SARS-CoV-2 induced metabolic derangement differed with infection severity. The metabolic changes were identified in saliva and plasma, however, saliva showed higher intensity of metabolic changes. Levels of saliva metabolites such as sphingosine and kynurenine were significantly different between COVID-19 infected and non-infected individuals; while linoleic acid and Alpha-ketoisovaleric acid were specifically increased in severe compared to non-severe patients. As expected, the two prognostic biomarkers of C-reactive protein and D-dimer were negatively correlated with sphingosine and 5-Aminolevulinic acid, and positively correlated with L-Tryptophan and L-Kynurenine. CONCLUSION: Saliva disease-specific and severity-specific metabolite could be employed as potential COVID-19 diagnostic and prognostic biomarkers.

Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling.

Since its emergence as a pandemic in March 2020, coronavirus disease (COVID-19) outcome has been explored via several predictive models, using specific clinical or biochemical parameters. In the current study, we developed an integrative non-linear predictive model of COVID-19 outcome, using clinical, biochemical, immunological, and radiological data of patients with different disease severities. Initially, the immunological signature of the disease was investigated through transcriptomics analysis of nasopharyngeal swab samples of patients with different COVID-19 severity versus control subjects (exploratory cohort, n=61), identifying significant differential expression of several cytokines. Accordingly, 24 cytokines were validated using a multiplex assay in the serum of COVID-19 patients and control subjects (validation cohort, n=77). Predictors of severity were Interleukin (IL)-10, Programmed Death-Ligand-1 (PDL-1), Tumor necrosis factors-α, absolute neutrophil count, C-reactive protein, lactate dehydrogenase, blood urea nitrogen, and ferritin; with high predictive efficacy (AUC=0.93 and 0.98 using ROC analysis of the predictive capacity of cytokines and biochemical markers, respectively). Increased IL-6 and granzyme B were found to predict liver injury in COVID-19 patients, whereas interferon-gamma (IFN-γ), IL-1 receptor-a (IL-1Ra) and PD-L1 were predictors of remarkable radiological findings. The model revealed consistent elevation of IL-15 and IL-10 in severe cases. Combining basic biochemical and radiological investigations with a limited number of curated cytokines will likely attain accurate predictive value in COVID-19. The model-derived cytokines highlight critical pathways in the pathophysiology of the COVID-19 with insight towards potential therapeutic targets. Our modeling methodology can be implemented using new datasets to identify key players and predict outcomes in new variants of COVID-19.

Asthma Associated Cytokines Regulate the Expression of SARS-CoV-2 Receptor ACE2 in the Lung Tissue of Asthmatic Patients.

It is still controversial whether chronic lung inflammation increases the risk for COVID-19. One of the risk factors for acquiring COVID-19 is the level of expression of SARS-CoV-2 entry receptors, ACE2 and TMPRSS2, in lung tissue. It is, however, not clear how lung tissue inflammation affects expression levels of these receptors. We hence aimed to determine the level of SARS-CoV-2 receptors in lung tissue of asthmatic relative to age, gender, and asthma severity, and to investigate the factors regulating that. Therefore, gene expression data sets of well-known asthmatic cohorts (SARP and U-BIOPRED) were used to evaluate the association of ACE2 and TMPRSS2 with age, gender of the asthmatic patients, and also the type of the underlying lung tissue inflammatory cytokines. Notably, ACE2 and to less extent TMPRSS2 expression were upregulated in the lung tissue of asthmatics compared to healthy controls. Although a differential expression of ACE2, but not TMPRSS2 was observed relative to age within the moderate and severe asthma groups, our data suggest that age may not be a key regulatory factor of its expression. The type of tissue inflammation, however, associated significantly with ACE2 and TMPRSS2 expression levels following adjusting with age, gender and oral corticosteroids use of the patient. Type I cytokine (IFN-γ), IL-8, and IL-19 were associated with increased expression, while Type II cytokines (IL-4 and IL-13) with lower expression of ACE2 in lung tissue (airway epithelium and/or lung biopsies) of moderate and severe asthmatic patients. Of note, IL-19 was associated with ACE2 expression while IL-17 was associated with TMPRSS2 expression in sputum of asthmatic subjects. In vitro treatment of bronchial fibroblasts with IL-17 and IL-19 cytokines confirmed the regulatory effect of these cytokines on SARS-CoV-2 entry receptors. Our results suggest that the type of inflammation may regulate ACE2 and TMPRSS2 expression in the lung tissue of asthmatics and may hence affect susceptibility to SARS-CoV-2 infection.

Prediction of COVID-19 Hospital Length of Stay and Risk of Death Using Artificial Intelligence-Based Modeling.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly infectious virus with overwhelming demand on healthcare systems, which require advanced predictive analytics to strategize COVID-19 management in a more effective and efficient manner. We analyzed clinical data of 2017 COVID-19 cases reported in the Dubai health authority and developed predictive models to predict the patient's length of hospital stay and risk of death. A decision tree (DT) model to predict COVID-19 length of stay was developed based on patient clinical information. The model showed very good performance with a coefficient of determination R 2 of 49.8% and a median absolute deviation of 2.85 days. Furthermore, another DT-based model was constructed to predict COVID-19 risk of death. The model showed excellent performance with sensitivity and specificity of 96.5 and 87.8%, respectively, and overall prediction accuracy of 96%. Further validation using unsupervised learning methods showed similar separation patterns, and a receiver operator characteristic approach suggested stable and robust DT model performance. The results show that a high risk of death of 78.2% is indicated for intubated COVID-19 patients who have not used anticoagulant medications. Fortunately, intubated patients who are using anticoagulant and dexamethasone medications with an international normalized ratio of <1.69 have zero risk of death from COVID-19. In conclusion, we constructed artificial intelligence-based models to accurately predict the length of hospital stay and risk of death in COVID-19 cases. These smart models will arm physicians on the front line to enhance management strategies to save lives.