Review of T cell receptor repertoire signatures associated with COVID-19 severity

This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/5933728.

Main Claim & Relevance:

In this preprint by Park et al., a large scale analysis of T cell receptor repertoire signatures was performed in order to link the TCR repertoire signature to COVID-19 infection severity. Antigen exposure from COVID-19 significantly decreased the diversity of repertoires and reshaped clonal representation. Machine learning algorithms were then trained on the data obtained from the TCR repertoires, in order to predict COVID-19 infection severity in patients. These algorithms were able to accurately predict the severity of a COVID case, however they were more effective in predicting mild and moderate disease than severe disease.

Are the findings strong, reliable, potentially informative, not informative, or misleading?

The findings are reliable. The TCR samples were obtained from several different groups from around the world, and the sample size of 2,130 individuals adds to the strength of the findings. The use of the data obtained through TCR repertoires in machine learning algorithms will need to be confirmed through further studies, as well as through the use of additional algorithms.

How might these ideas presented by the main claims further knowledge of the COVID-19 Pandemic?

Currently, there is a lack of literature regarding TCR specificity groups for COVID-19, and this paper provides useful information as to the impact of COVID antigens on TCR repertoires. This study claims that TCR repertoire data can be used alongside machine learning algorithms to predict the severity of a COVID-19 case. If this is the case, this could prove to be a powerful prognostic tool and aid in patient care, however these findings will need to be verified through additional studies.