Complement activation to predict Sars-CoV-2 outcome

Summary

The recent Covid-19 outbreak is one of the greatest challenges we face. Critical to how we manage this disease as we go forward is how we identify who is at risk of developing serious disease requiring hospitalisation, and who will likely have relatively minor or indeed no symptoms. This is key to management not only of cases, but also how we manage future outbreaks in respect of shielding and protecting the right groups of people.

One thing which has become clear is that those who have the most severe disease undergo a very strong immune response, which damages host tissues. A key part of this response is likely to be the complement system.

Complement is continually monitoring for disease and acts as a ‘first line’ of defence against pathogens, recognising and killing virus-infected cells and recruiting more sophisticated cells to generate antibodies etc. It is likely that the level to which complement is activated plays a significant role in the size of the immune response and therefore can predict disease severity. This is true of other infectious diseases, and indeed other strains of coronavirus infection, where suppressing complement reduces cytokine levels and symptoms.

In this study, we wish to use plasma samples taken from both healthy individuals, and those infected with Covid-19 who have a range of outcomes (from no symptoms to requiring ventilation) and study how complement activation drives these outcomes.

To do this, we have developed a new method which can simultaneously measure all components of the main activating pathway of the complement system – some 18 different molecules, all of which have unique functions in modifying the response to disease – to build a picture of whether and how activation of the complement system predicts severity of disease in patients infected with Covid-19.

Participation: For this study, blood samples and data from COVID-19 BioResource volunteers were provided. 

Organisation: This study is organised by Dr Richard Unwin from the University of Manchester