Using ex-vivo genetic systems as models to detect genetic susceptibility to co-morbidities in Covid 19 infection, and to test specific anti-inflammatory / antiviral drugs in Covid 19 infection

Research has shown that certain individuals are more susceptible to SARS-CoV-2. Approximately, 20–51% of COVID-19 patients presented with a least one co-morbidity on admission, with diabetes (10–20%) hypertension (10–15%) and other cardiovascular and cerebrovascular diseases (7–40%) being most prevalent. Thus, elucidating that patients with pre-existing co-morbidity have poorer clinical outcomes than those without. Additionally, a greater number of co-morbidities also correlated with poorer clinical outcomes (Guan, Liang et al. 2020).

To date, there has been a lack of knowledge regarding several aspects of SARS-CoV-2 infection, from pathogen biology to host response and treatment options. We believe that adequately understanding human genetic variation in response to the virus, offer important insights into the treatment and management of the disease, including the identification of new therapies. We aim to determine differences in DNA susceptibility of the different groups to damage as a biomarker for circulating genotoxicity, which in turn will be a reflection of disease susceptibility. The understanding of the genetic basis and human disease, including single-gene disorders, (Jackson, Marks et al. 2018). We will use deep sequencing to characterize the transcriptome of our lymphocytes from patients compared to healthy control. We will use freely available, open-source bioinformatics tools to perform differential gene expression and pathway enrichment analysis. We hypothesize that cells viability (and its loss) in ex-vivo would be reflected in the differences in transcription of genes involved in pathways related to cell death.