Memory CD4+ T cell receptor repertoire data mining as a tool for identifying cytomegalovirus serostatus

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Abstract
Pathogens of past and current infections have been identified directly by means of PCR or indirectly by measuring a specific immune response (e.g., antibody titration). Using a novel approach, Emerson and colleagues showed that the cytomegalovirus serostatus can also be accurately determined by using a T cell receptor repertoire data mining approach. In this study, we have sequenced the CD4+ memory T cell receptor repertoire of a Belgian cohort with known cytomegalovirus serostatus. A random forest classifier was trained on the CMV specific T cell receptor repertoire signature and used to classify individuals in the Belgian cohort. This study shows that the novel approach can be reliably replicated with an equivalent performance as that reported by Emerson and colleagues. Additionally, it provides evidence that the T cell receptor repertoire signature is to a large extent present in the CD4+ memory repertoire.