RT Journal Article
SR Electronic
T1 Plasma proteome dynamics of COVID-19 severity learnt by a graph convolutional network of multi-scale topology
JF Life Science Alliance
JO Life Sci. Alliance
FD Life Science Alliance LLC
SP e202201624
DO 10.26508/lsa.202201624
VO 6
IS 5
A1 Samy Gauthier
A1 Alexy Tran-Dinh
A1 Ian Morilla
YR 2023
UL https://www.life-science-alliance.org/content/6/5/e202201624.abstract
AB Efforts to understand the molecular mechanisms of COVID-19 have led to the identification of ACE2 as the main receptor for the SARS-CoV-2 spike protein on cell surfaces. However, there are still important questions about the role of other proteins in disease progression. To address these questions, we modelled the plasma proteome of 384 COVID-19 patients using protein level measurements taken at three different times and incorporating comprehensive clinical evaluation data collected 28 d after hospitalisation. Our analysis can accurately assess the severity of the illness using a metric based on WHO scores. By using topological vectorisation, we identified proteins that vary most in expression based on disease severity, and then utilised these findings to construct a graph convolutional network. This dynamic model allows us to learn the molecular interactions between these proteins, providing a tool to determine the severity of a COVID-19 infection at an early stage and identify potential pharmacological treatments by studying the dynamic interactions between the most relevant proteins.