RE: SNG in a nutshell16 Apr 2021 11:26
Hi Ghia and anyone else interested
Here is the full lancet paper - https://www.sciencedirect.com/science/article/pii/S2352396421001328
Fantastic work by these scientists creating a genetic biomarker model to predict disease severity. Will be known as the EPICOVID signature. Essentially they ran tests on people and performed a genetic test They found 20 genes were linked to severe outcomes of Covid, 7 of which were linked to interferon.
Significantly overrepresented genes (hypergeometric test, P value = 2.14e-06) associated with GO biological processes included those of “immune response”, “type I interferon signalling pathway”, “interferon-gamma-mediated signalling pathway”, “antigen processing and presentation”, “defence response to virus”, “cytokine-mediated signalling pathway” and “inflammatory response”. These processes are highly relevant to the degree of potential response to COVID-19 infection, since they are related to the capacity of the immune system to respond to viral infections. In this regard, the 20 genes containing the CpG methylation variants associated with COVID-19 severity include an overrepresentation of genes that mediate the response to interferon, a key pathway in the physiopathological pathway of the disease
* 13% of people in the population match the EPICOVID signature and in theory means they will likely develop severe Covid.
* In hospital they found between 83.5 and 90% of patients with severe Covid matched the EPICOVID signature.
Using this approach, we obtained an epigenomic signature, hereafter referred to as the EPICOVID signature, that predicted COVID-19 severity with 90.18% accuracy (mean Kappa = 0.804). Supervised hierarchical clustering using the COVID-19 signature also clearly distinguished two branches that were significantly enriched with respect to each condition: those who were asymptomatic/paucisymptomatic and those with respiratory failure. We observed that the EPICOVID-positive signature was also associated with worse COVID-19 clinical course with a specificity of 88.18%, a sensitivity of 77.78%, and positive and negative predictive values (PPV and NPV) of 84.34% and 82.91%, respectively. The accuracy was 83.5% and the mean Kappa 0.6643.
* Regards to using this testing in a hospital setting as a predictor of disease outcomes then the answer in theory is yes.
The use of more user-friendly PCR approaches, such as the described pyrosequencing technology, could facilitate the analyses at the common hospital laboratory level. In this regard, it is worth noting that, if instead of the comprehensive EPICOVID signature we selected the top five CpG sites associated with severity according to P-value (Table S2), its single differential methylation status was still ossociated with COVID-19 severity (69%-76% accuracy range). Thus, a more restricted signature derived from the heatmap and clustering analyses might be useful in future prospective multicentre studies.