Comparative analysis of immune-associated genes in COVID-19, cardiomyopathy and venous thromboembolism (preprint)

As of 28 August 2020, there have been 5.88 million Coronavirus Disease 2019 (COVID-19) cases and 181,000 COVID-19 related deaths in the United States alone. Given the lack of an effective pharmaceutical treatment for COVID-19, the high contagiousness of the disease and its varied clinical outcomes, identifying patients at risk of progressing to severe disease is crucial for the allocation of valuable healthcare resources during this pandemic. Current research has shown that there is a higher prevalence of cardiovascular comorbidities amongst patients with severe COVID-19 or COVID-19-related deaths, but the link between cardiovascular disease and poorer prognosis is poorly understood. We believe that pre-existing immune dysregulation that accompanies cardiovascular disease predisposes patients to a harmful inflammatory immune response, leading to their higher risk of severe disease. Thus, in this project, we aim to characterize immune dysregulation in patients with cardiomyopathy, venous thromboembolism and COVID-19 patients by looking at immune-associated gene dysregulation, immune infiltration and dysregulated immunological pathways and gene signatures.

Tobacco, but not nicotine and flavor-less electronic cigarettes, induces ACE2 and immune dysregulation (preprint)

COVID-19, caused by the virus SARS-CoV-2, has infected millions worldwide. This pandemic overlaps with the ongoing epidemics of cigarette smoking and electronic cigarette (e-cig) vaping, with over 1 billion smokers and vapers worldwide. However, there is scarce data relating COVID-19 risks and outcome with cigarette or e-cig use. In this study, we mined 3 independent RNA expression datasets from smokers and vapers to understand the potential relationship between vaping/smoking and the dysregulation of key genes and pathways related to COVID-19. We found that smoking, but not vaping, upregulates ACE2, the cellular receptor that SARS-CoV-2 requires for infection. Both smoking and use of nicotine and flavor-containing e-cig led to upregulations of pro-inflammatory cytokine production and expression of genes related to inflammasomes. Vaping flavor-less and nicotine-less e-cig, however, did not lead to significant cytokine dysregulation and inflammasome activation. Release of inflammasome products, such as IL-1B, and cytokine storms are hallmarks of COVID-19 infection, especially in severe cases. Therefore, our findings demonstrated that smoking or vaping, specifically use of flavored or nicotine-containing e-cigs, may critically exacerbate COVID-19-related inflammation or increase susceptibility to the disease. Further scientific and public health investigations should be undertaken to address these concerning links between COVID-19 and e-cig/smoking.

Using Machine Learning of Clinical Data to Diagnose COVID-19 (preprint)

The recent pandemic of Coronavirus Disease 2019 (COVID-19) has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests. In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aimed to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID -19 patients and influenza patients based on clinical variables alone. We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients. We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups.